# collections

id: 9f6830d5-28e2-4183-8dab-4dc5c96fa931
name: courses
database_id: 00000000-0000-0000-0000-000000000000
config_json_str:
{"hnsw_configuration": {"space": "l2", "ef_construction": 100, "ef_search": 10, "num_threads": 12, "M": 16, "resize_factor": 1.2, "batch_size": 100, "sync_threshold": 1000, "_type": "HNSWConfigurationInternal"}, "_type": "CollectionConfigurationInternal"}


# embedding_metadata

key: url
string_value:
https://courses.analyticsvidhya.com/courses/coding-a-chatgpt-style-language-model-from-scratch-in-pytorch


key: chroma:document
string_value:
            Title: Coding a ChatGPT-style Language Model from Scratch in PyTorch
            Description: Master the art of building a ChatGPT-style language model from scratch with PyTorch. In this course, you'll explore essential NLP concepts, implement transformers, and create a decoder-only architecture step-by-step. Guided by expert tutorials, gain practical skills to develop advanced AI models tailored for real-world applications.
            Curriculum: Introduction | Importing | Creating Inputs & Lables | Position Encoding | Masked Self-Attention | Putting Pieces Together | Creating Decoder Only Transformer
            Key Takeaways: Ideal for those starting their journey in machine learning and eager to understand NLP and transformer models in-depth. | For developers with some experience in Python or PyTorch, looking to level up by building ChatGPT-like language models. | Those interested in expanding their skill set to include deep learning techniques and cutting-edge language modeling. | Anyone passionate about AI advancements, eager to learn practical techniques for creating state-of-the-art NLP solutions. | Understand transformer fundamentals with step-by-step guidance, including masked self-attention and position encoding. | Build and train models using PyTorch, focusing on practical coding skills for NLP. | Create a fully functional ChatGPT-style language model from scratch, ready for real-world use.
            FAQs: Q: What prior knowledge is required for this course? A: Basic knowledge of Python and machine learning fundamentals is recommended. Familiarity with PyTorch is helpful but not mandatory, as the course includes step-by-step guidance. | Q: How is the course delivered? A: The course features video tutorials, hands-on coding exercises, and downloadable resources, allowing you to learn at your own pace. | Q: Will I build a complete ChatGPT-style model by the end? A: Yes! The course walks you through the entire process, from data preparation to implementing and training a decoder-only transformer. | Q: Is this course suitable for beginners? A: Absolutely! The course is beginner-friendly, breaking down complex topics into manageable steps, with clear explanations and practical examples.
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/mastering-multilingual-genai-open-weights-for-indic-languages


key: chroma:document
string_value:
            Title: Mastering Multilingual GenAI Open-Weights for Indic Languages
            Description: Mastering Multilingual GenAI – Open-Weights for Indic Languages" is a course designed to equip you with the knowledge to develop state-of-the-art multilingual AI models using open-weight architectures. Focusing on low-resource languages, particularly Indic languages, the course covers essential topics like multilingual AI training, instruction fine-tuning, model building, and performance evaluation.
            Curriculum: Introduction | Importance of Multilingual | Training for Multilingual Gen AI | Instruction Fine-Tuning Data for Multilingual | Measuring Performance for Multilingual | Building a Model | Human Preferences | Curse of Multilinguality | Coding Hands-On
            Key Takeaways: Those looking to build state-of-the-art multilingual models for low-resource languages. | Tech Entrepreneurs and Innovators who want to build scalable, inclusive AI systems that cater to a diverse, multilingual user base. | Data Scientists seeking to explore state-of-the-art instruction fine-tuning techniques and work with large multilingual datasets. | Students and Professionals aspiring to work in cutting-edge AI fields, with a specific interest in bias mitigation, safety, and ethical considerations in language models. | Learn to create AI models for diverse languages, focusing on low-resource ones. | Get hands-on experience with cutting-edge models and multilingual data. | Implement strategies for safer, unbiased AI models in global applications.
            FAQs: Q: What prior knowledge is required for this course? A: A basic understanding of AI and machine learning concepts is recommended. Familiarity with natural language processing (NLP) will be helpful but is not mandatory. | Q: What tools and technologies will I learn in this course? A: You'll work with state-of-the-art open-weight models, instruction fine-tuning techniques, and the Aya dataset. | Q: How does this course address low-resource languages? A: The course focuses on training models that perform well on low-resource languages, particularly Indic languages, through the use of the Aya dataset, multilingual instruction fine-tuning, and bias mitigation techniques. | Q: Will there be hands-on projects? A: Yes, the course includes practical coding exercises and real-world projects where you will build and fine-tune multilingual AI models, with a focus on tackling real-world challenges in low-resource language modeling.
            


key: chroma:document
string_value:
            Title: Learning Autonomous Driving Behaviors with LLMs & RL
            Description: This course dives into the development of autonomous driving behaviors using Reinforcement Learning (RL) and Large Language Models (LLMs). You’ll explore how RL agents are trained to navigate complex, real-world environments while making safe, human-like driving decisions. The course tackles key challenges such as designing effective reward systems, ensuring safety in high-speed driving scenarios, and improving the interpretability of AI decisions. Through practical projects, you will design RL agents using techniques like Deep Q-Networks (DQN), experience replay, and integrate LLMs to enhance decision-making.
            Curriculum: Introduction | Evolution RL | Understanding RL | Challenges with RL | Approach to the Problem Statement | Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL
            Key Takeaways: Professionals and students interested in AI, autonomous systems, and machine learning. | Engineers and developers looking to apply RL and LLMs in real-world autonomous driving projects. | Anyone seeking to explore cutting-edge AI technologies and their applications in safety-critical systems. | Learn how to train RL agents for safe, human-like autonomous driving behavior. | Understand how LLMs enhance decision-making and interpretability in AI systems. | Gain practical experience in designing and applying reward functions for real-world autonomous environments.
            FAQs: Q: What is the focus of this course? A: This course focuses on using Reinforcement Learning (RL) and Large Language Models (LLMs) to train autonomous driving systems. You'll learn how to develop RL agents that make safe, human-like driving decisions in complex environments like highways. | Q: How are LLMs used in this course? A: LLMs are integrated to guide RL agents by enhancing their decision-making capabilities, particularly in designing reward systems that align with human behavior. | Q: What practical skills will I gain? A: You will gain hands-on experience in training RL agents using techniques like Deep Q-Networks (DQN) and experience replay. Additionally, you'll learn how to design reward systems and apply them to real-world autonomous driving scenarios. | Q: Is prior experience with RL or AI required? A: Prior experience with AI or RL would be beneficial. | Q: What challenges in autonomous driving are addressed? A: The course tackles key challenges such as reward hacking, ensuring safety in high-speed driving environments, and the "black box" nature of AI decisions. You will learn strategies to overcome these issues using RL and LLMs.
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/genai-applied-to-quantitative-finance-for-control-implementation


key: chroma:document
string_value:
            Title: GenAI Applied to Quantitative Finance: For Control Implementation
            Description: This course explores the application of Generative AI in quantitative finance, focusing on building sustainable trading algorithms through keyword extraction, sentiment analysis, and time-series forecasting. Learn to predict commodity prices, such as gold, by integrating data from financial news sources, leveraging sentiment analysis, and optimizing models for robust trading signals.
            Curriculum: Introduction | Overview | Problem Definition: Commodity Price Prediction | Architecture | Hands-On
            Key Takeaways: Learn how to apply advanced AI techniques to convert textual data, such as news articles, into actionable trading signals for predicting commodity prices. | Understand the importance of a well-structured architecture that includes robust keyword extraction, sentiment mining, graph generation, and time series forecasting to drive predictive accuracy. | Explore opportunities for further improvements, such as integrating large language models (LLMs), enhancing robustness, and achieving full automation in trading signal generation.
            FAQs: 
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/navigating-llm-tradeoffs-techniques-for-speed-cost-scale-and-accuracy


key: chroma:document
string_value:
            Title: Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy
            Description: This course provides a concise guide to optimizing Large Language Models (LLMs) by navigating tradeoffs in speed, cost, scale, and accuracy. Learn practical techniques like LoRA, model quantization, and parameter-efficient fine-tuning to improve performance while reducing costs. You'll explore various deployment strategies and understand how to evaluate LLMs using industry-standard benchmarks, making this course ideal for anyone seeking efficient, scalable AI solutions.
            Curriculum: Introduction | Resources | Technique to Increase Accuracy | Training Speed and Cost Optimization | Inference Speed and Cost Optimization | Scale
            Key Takeaways: ML Engineers and Data Scientists seeking to optimize LLMs for efficient deployment. | AI Enthusiasts interested in learning practical techniques to balance LLM performance, cost, and scalability. | Professionals in MLOps aiming to understand different deployment strategies for LLMs, including cloud, containerized, and serverless options. | Optimize LLM Tradeoffs: Master techniques to balance speed, cost, scale, and accuracy for LLMs. | Efficient Fine-Tuning: Use LoRA to train large models with less compute while maintaining performance. | Model Quantization: Reduce memory use and boost inference speed with 8-bit/4-bit quantization. | LLM Evaluation Metrics: Assess models with ROUGE, BLEU, and benchmark tools like HuggingFace Leaderboard. | Inference Optimization: Multi-LoRA, Improve efficiency with KV caching and Flash attention for faster results. | Flexible Deployments: Choose from APIs, Kubernetes, and serverless options for scalable LLM deployment. | Cost-Effective scaling and testing: Leveraging different cloud solutions based on use-case and how to load test applications for real-world scenario.
            FAQs: Q: What will I learn from this course? A: You'll learn how to navigate tradeoffs in LLMs, including techniques for improving speed, reducing costs, scaling models, and optimizing accuracy, as well as deployment and fine-tuning methods. | Q: Do I need prior experience with LLMs to take this course? A: Basic knowledge of machine learning concepts and coding is helpful, but the course covers LLM fundamentals, making it accessible to those new to LLMs. | Q: How is fine-tuning covered in the course? A: The course focuses on efficient fine-tuning methods like LoRA, which enable you to adapt large models cost-effectively with minimal hardware requirements. | Q: What practical skills will I gain? A: You'll gain hands-on skills in LLM evaluation, model quantization, prompt engineering, and deploying models using various cloud and on-premise solutions. | Q: How can this course help me reduce LLM deployment costs? A: You'll learn techniques like quantization, spot instance utilization, and serverless deployments to significantly cut down on costs without sacrificing model performance.
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/creating-problem-solving-agents-using-genai-for-action-composition


key: chroma:document
string_value:
            Title: Creating Problem-Solving Agents using GenAI for Action Composition
            Description: This introductory course provides a concise overview of Agentic AI systems, covering their evolution, current state, and practical applications. You will explore key topics including the history of Agentic AI systems, the role of agents today, multi-agent systems, and practical solutions for implementing them. Perfect for those seeking a foundational understanding of intelligent Agentic AI systems in action.
            Curriculum: Introduction | Overview- Count the Number of Agents | A brief history of Agentic Systems | Agents Today | Multi-Agent Systems Today | Practical Solutions
            Key Takeaways: Beginners in AI and ML looking to understand agentic systems and their real-world applications. | Tech enthusiasts and developers interested in learning the basics of creating intelligent, problem-solving agents. | Professionals exploring multi-agent systems for automation and dynamic task orchestration in various domains. | Understand the evolution and current capabilities of agentic systems. | Learn the basics of agents and multi-agent systems, including their role in solving complex problems. | Gain insights into practical applications of agents and how they function in real-world scenarios.
            FAQs: Q: What is an agentic system? A: An agentic system refers to an AI-based structure where agents can autonomously perform tasks, often in collaboration, to solve complex problems. | Q: Do I need prior AI knowledge to take this course? A: No, this course is beginner-friendly and provides an introduction to agentic systems and their applications. | Q: Are there any hands-on projects in this course? A: Yes, you will see practical demos that illustrate how agents are created and used in real-world applications. | Q: How will this course benefit me professionally? A: You'll gain foundational knowledge of AI agents, enabling you to apply these concepts to automation, AI development, and other tech projects involving intelligent systems.
            


key: chroma:document
string_value:
            Title: Improving Real World RAG Systems: Key Challenges & Practical Solutions
            Description: This course explores the key challenges in building real-world Retrieval-Augmented Generation (RAG) systems and provides practical solutions. Topics include improving data retrieval, dealing with hallucinations, context selection, and optimizing system performance using advanced prompting, retrieval strategies, and evaluation techniques. Through hands-on demos, you will gain insights into better chunking, embedding models, and agentic RAG systems for more robust, real-world applications.
            Curriculum: Introduction to RAG Systems | Resources | RAG System Challenges Practical Solutions | Hands-on: Solution for Missing Content in RAG | Other Key Challenges | Practical Solutions | Hands-on: Solution for Missed Top Ranked, Not in Context, Not Extracted _ Incorrect SpecificityHands-on- Solution for Missed | Wrong Format Problem  Solution | Hands-on: Solution for Wrong Format | Incomplete Problem Solution | HyDE | Other Practical Solutions from recent Research Papers
            Key Takeaways: AI/ML professionals aiming to enhance RAG system performance and solve real-world challenges. | Developers/Engineers building search, conversational, or generative AI systems needing better data retrieval and context handling. | Researchers/Enthusiasts seeking hands-on experience with advanced RAG techniques and agentic systems. | Master RAG systems with a solid grasp of architecture and components. | Solve key challenges like missing content and hallucinations. | Optimize performance with advanced chunking and retrieval strategies. | Develop practical decision-making skills for LLM adoption in various industries.
            FAQs: Q: What prior knowledge is required? A: A basic understanding of AI/ML principles is needed, along with some experience in machine learning frameworks such as PyTorch or TensorFlow. Familiarity with natural language processing (NLP) concepts will be helpful but not mandatory. | Q: Will there be hands-on exercises? A: Yes, the course provides practical, hands-on exercises through demos and notebooks. You’ll have opportunities to implement RAG systems and experiment with real-world use cases, focusing on improving retrieval and generation tasks. | Q: What tools or software will I need? A: You’ll need access to Python, Jupyter Notebooks, and relevant libraries such as LangChain, Hugging Face, and vector databases. The course will guide you through setting up the necessary environment for practicing the techniques. | Q: How is this course different from other AI courses? A: Unlike general AI/ML courses, this course zeroes in on Retrieval-Augmented Generation (RAG) systems, addressing practical challenges like hallucinations, retrieval errors, and context optimization, with a strong emphasis on real-world applications. | Q: Will I learn advanced techniques? A: Yes, the course covers advanced techniques like hyperparameter tuning, chunking strategies, embedding models, context compression, and agentic RAG systems, giving you the tools to build and optimize high-performing RAG systems.
            


key: chroma:document
string_value:
            Title: Framework to Choose the Right LLM for your Business
            Description: This course will guide you through the process of selecting the most suitable Large Language Model (LLM) for various business needs. By examining factors such as accuracy, cost, scalability, and integration, you will understand how different LLMs perform in specific scenarios, from customer support to healthcare and strategy development. The course emphasizes practical decision-making with real-world case studies, helping businesses navigate the rapidly evolving LLM landscape effectively.
            Curriculum: Introduction | It's an LLM World! | Understand Your Business | Framework to Choose the Right LLM | Case Studies | Conclusion
            Key Takeaways: Business leaders seeking to implement AI-driven solutions efficiently. | Data scientists exploring LLMs for industry-specific applications. | Tech professionals involved in AI integration and decision-making processes. | Understand how to evaluate and select the right LLM for business needs. | Learn to assess LLMs based on accuracy, cost, scalability, and integration. | Gain insights into real-world LLM applications through case studies. | Develop practical decision-making skills for LLM adoption in various industries.
            FAQs: Q: What factors should be considered when choosing an LLM for business? A: Key factors include the LLM's accuracy, cost, scalability, technical compatibility, support, security, and compliance with privacy laws. The decision-making framework ensures the chosen LLM aligns with specific business requirements. | Q: Can any LLM solve all business problems? A: No, different LLMs are suited to different tasks. Selecting the right LLM depends on the specific business problem, required capabilities, and available resources. | Q: How important is the accuracy of an LLM for business use? A: Accuracy is crucial, especially in fields like healthcare and education. LLMs must perform reliably across datasets, ensuring consistency and stability in results for critical business applications. | Q: What are the key decisions for using LLMs in healthcare? A: Key decisions include choosing an LLM fine-tuned for medical data, ensuring accuracy, maintaining privacy, and complying with healthcare regulations. | Q: Are open-source LLMs viable alternatives to closed-source options? A: Yes, open-source LLMs, like Llama3, can be viable alternatives, especially when customized to specific business needs. They are catching up with closed-source options in performance.
            


key: chroma:document
string_value:
            Title: Building Smarter LLMs with Mamba and State Space Model
            Description: Unlock the Power of State Space Models (SSM) like Mamba with our comprehensive course designed for AI professionals, data scientists, and NLP enthusiasts. Master the art of integrating SSM with deep learning, unravel the complexities of models like Mamba, and elevate your understanding of Generative AI's newest and most innovative models. This course is designed to equip you with the skills needed to understand these cutting-edge AI models and how they work, making you proficient in the latest AI techniques and architectures.
            Curriculum: Course Overview | Are RNNs a Solution | The Problem with Transformers | What is a State Space Model? | The Discrete Representation | The Recurrent Representation | The Convolution Representation | The Three Representations | The Importance of the A Matrix | What Problem does it attempt to Solve? | Selectively Retaining Information | Speeding Up Computations | Exploring the Mamba Block | Jamba - Mixing Mamba with Transformers
            Key Takeaways: AI and ML professionals looking to specialize in State Space Models and Mamba architecture. | Data scientists interested in exploring advanced Generative AI models and architectures. | NLP practitioners who want to integrate SSMs like Mamba in their workflows and use cases. | A comprehensive understanding of State Space Models (SSM) | In-depth exploration of The Mamba Architecture | Visual guides and workflows on SSM and Mamba | Advanced applications, comparisons and practical use cases.
            FAQs: Q: What are State Space Models (SSM) in machine learning? A: State Space Models (SSM) are used in machine learning to model and predict systems that evolve over time. They represent the system's state as a dynamic process, helping to capture temporal patterns in data, making them useful for tasks like time series forecasting, control systems, and natural language processing. | Q: How do State Space Models differ from traditional RNNs? A: State Space Models (SSM) and traditional Recurrent Neural Networks (RNNs) both handle sequential data, but they differ in approach. SSMs use a mathematical framework to model the system's state and evolution over time explicitly. In contrast, RNNs use neural networks to implicitly learn patterns in sequences without explicitly modeling the system's state. | Q: What is the Mamba architecture in AI? A: Mamba is an alternative AI architecture designed to address the limitations of traditional transformers. It enhances efficiency with optimizations like RMSnorm and offers significant improvements in inference speed—up to 5× higher throughput. Mamba also scales linearly with sequence length, making it highly effective for handling real-world data, even with sequences up to a million tokens. As a versatile backbone, Mamba achieves state-of-the-art performance across various domains, including language, audio, and genomics. Notably, the Mamba-3B model outperforms transformers of the same size and rivals those twice its size in both pretraining and downstream evaluation. | Q: How does Mamba compare to transformer models? A: Mamba architecture differs from traditional transformer models by leveraging state-space models (SSMs) instead of the self-attention mechanism. This key difference allows Mamba to achieve linear complexity scaling with sequence length, a significant improvement over the quadratic scaling seen in transformers. While transformers excel in parallel processing with self-attention, Mamba's use of SSMs enables it to handle sequences more efficiently, especially in tasks involving long sequences, while still supporting parallel processing during training. | Q: What are the applications of State Space Models in NLP? A: State Space Models (SSM) are used in NLP for similar applications as other Language Models (LLMs), such as predicting and modeling sequential language patterns. However, SSMs stand out due to their ability to handle long text sequences more efficiently, making them particularly advantageous in tasks that involve processing extensive dependencies within the text. | Q: How do transformers work in large language models? A: Transformers use self-attention mechanisms to process input data in parallel, allowing large language models to efficiently learn relationships between words in a sequence, improving performance in tasks like translation and text generation. | Q: Why are RNNs considered in AI despite transformer popularity? A: RNNs are still used in AI because they excel at handling sequential data with strong temporal dependencies, and their simpler architecture can be advantageous in specific applications where transformers might be overkill. | Q: What are the benefits of using Mamba in deep learning? A: Mamba architecture offers improved efficiency in deep learning models, particularly in handling complex tasks and large-scale AI applications, making it a powerful alternative to traditional transformers. | Q: How do State Space Models improve the accuracy of AI models, such as RNNs or Transformers in processing temporal data? A: State Space Models improve AI accuracy by explicitly modeling the underlying state of a system over time, leading to better predictions and more interpretable results, especially in time-sensitive applications.
            


key: chroma:document
string_value:
            Title: Generative AI - A Way of Life - Free Course
            Description: This course is a transformative journey tailored for beginners and delves into AI-powered text and image generation using leading tools like ChatGPT, Microsoft Copilot, and DALL·E3. Learn practical applications across industries, ethical considerations, and best practices. Whether you're a content creator, business innovator, or AI enthusiast, gain the expertise to harness Generative AI's full potential and drive innovation in your field.
            Curriculum: Fundamentals of Generative AI | What is Generative AI? | How does Generative AI work? | Exploring the Potential of Generative AI | GenAI Pinnacle Program | Hands On: Let’s get generating! | An Overview of Text Generation | What is ChatGPT? | Working with ChatGPT | Working with ChatGPT Plus | Build a Custom GPT | Working with Microsoft Copilot | Working with Google Gemini | Gen AI Pinnacle Ad | Learning the Art of Prompting | Creating a Chatbot | Ethics and Best Practices | Introduction to Image Generation | Exploring the Potential of Image Generation | Working with free image generation tools | Working with Clipdrop | Working with Bing Image Creator | Working with Firefly | Working with Paid Image Generative Tools | Working with paid image generative tools DreamStudio | Working with DALLE-2 | Working with Midjourney | Gen AI Pinnacle Ad | Prompting your Way to Art | Accomplishing Tasks with Image Generation | E for Ethics and Efficiency
            Key Takeaways: Utilize leading AI tools like ChatGPT, Microsoft Copilot, and DALL·E3 to create text and image content, enhancing your ability to innovate and streamline your creative processes. | Explore practical use cases across various industries, from marketing to finance, and apply ethical best practices, ensuring you are well-equipped to implement AI solutions responsibly and effectively.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Building LLM Applications using Prompt Engineering - Free Course
            Description: This course will provide you with a hands-on understanding of building LLM applications and mastering prompt engineering techniques. By the end of the course, you will be proficient in implementing and fine-tuning these techniques to enhance generative AI model performance. You'll learn to apply various prompting methods and build chatbots on enterprise data, equipping you with the skills to improve conversational AI systems in real-world projects.
Who Should Enroll:

Professionals: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.
Aspiring Students: Individuals looking to deepen their knowledge and apply advanced LLM and prompt engineering techniques to solve complex problems across various domains.
            Curriculum: Introduction to Building Different LLM applications | Prompt Engineering | Retrieval Augmented Generation | Finetuning LLMs | Training LLMs from Scratch | Quiz | Course handouts | Introduction to Prompt Engineering | Set up your machine for Prompt Engineering | Prompt Engineering with ChatGPT API | Enabling Conversation with ChatGPT API | Quiz | Introduction to Understanding Different Prompt Engineering Techniques | Few Shot Prompting | One Shot Prompting | Zero Shot Prompting | Quiz | Assignment
            Key Takeaways: Learn to effectively build and implement different LLM applications | Understand and apply few-shot, one-shot, and zero-shot prompting | Gain expertise in building chatbots using enterprise data through advanced prompt engineering methods
            FAQs: 
            


key: chroma:document
string_value:
            Title: Building Your First Computer Vision Model - Free Course
            Description: This course will help you gain a deep understanding of Computer Vision and build advanced CV models using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming a Computer Vision expert. Master the techniques to build convolutional neural networks, and classify images.
Who Should Enroll:

Professionals: Individuals looking to expand their skill set and leverage CV across different industries.
Aspiring Students: For those setting out on their journey to master image data analysis and leave a mark in the tech world.
            Curriculum: Pixel Perfect - Decoding Images | Understanding a CNN - Convolutional Layer | Hands on - Image Processing Techniques | Understanding a CNN - Striding and Pooling | Understanding a CNN - Pooling Layer | Understanding AlexNet and Building a CNN Model | Quiz
            Key Takeaways: Learn Computer Vision techniques and build real-world CV Models. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Bagging and Boosting ML Algorithms - Free Course
            Description: This course will provide you with a hands-on understanding of Bagging and Boosting techniques in machine learning. By the end of the course, you will be proficient in implementing and tuning these ensemble methods to enhance model performance. You'll learn to apply algorithms like Random Forest, AdaBoost, and Gradient Boosting to a real-world dataset, equipping you with the skills to improve predictive accuracy and robustness in your projects.

Who Should Enroll:

Professionals: Individuals looking to deepen their knowledge and apply advanced machine learning techniques like Bagging and Boosting to solve complex problems across various domains
Aspiring Students: Individuals looking to deepen their knowledge and apply advanced ML techniques to bring value to businesses
            Curriculum: Resources to be used in this course | Problem Statement | Understanding Ensemble Learning | Introducing Bagging Algorithms | Hands-on to Bagging Meta Estimator | Introduction to Random Forest | Understanding Out-Of-Bag Score | Random Forest VS Classical Bagging VS Decision Tree | Project | Introduction to Boosting | AdaBoost Step-by-Step Explanation | Hands-on - AdaBoost | Gradient Boosting Machines (GBM) | Hands-on Gradient Boost | Other Algo (XGBoost, LightBoost. CatBoost) | Project: Anova Insurance
            Key Takeaways: Lear to effectively use Bagging and Boosting Algorithms | Hands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize these algorithms
            FAQs: 
            


key: chroma:document
string_value:
            Title: MidJourney: From Inspiration to Implementation - Free Course
            Description: This course will provide you with a practical understanding of MidJourney tools. By the end of the course, you will be able to utilize MidJourney effectively and explore alternative tools for your creative projects. You'll learn how to draw inspiration, use MidJourney's features, and understand its applications through engaging lessons.

Who Should Enroll:

Creative Professionals: Individuals looking to enhance their creativity and apply MidJourney tools to various artistic and visual projects.
Aspiring Creatives: Those beginning their journey into visual storytelling and digital art, seeking to learn the fundamentals of MidJourney and its alternatives.
            Curriculum: MidJourney - Storm _ Story | MidJourney - Inspiration | MidJourney - How to use | MidJourney  Alternatives | Quiz
            Key Takeaways: Understand MidJourney Tools: Gain a practical understanding of how to use MidJourney for creative projects. | Find Inspiration: Learn how to draw inspiration from various sources to fuel your creativity. | Explore Alternatives: Discover alternative tools to broaden your creative toolkit.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Understanding Linear Regression - Free Course
            Description: This free course will help you understand the fundamentals of linear regression in a straightforward manner. By the end of this course, you will be able to build predictive models using linear regression techniques. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering linear regression.
            Curriculum: Introduction to the Problem Statement | Resources for this Course | Introduction to Linear Regression | Significance of Slope and Intercept in the linear regression | How Model Decides The Best-Fit Line | Let’s Build a Simple Linear Regression Model | Model Understanding Using Descriptive Approach | Model Understanding Using Descriptive Approach - II | Model Building Using Predictive Approach | Quiz: Linear regression
            Key Takeaways: Learn Linear Regression technique and build real-world Machine Learning Models. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: The Working of Neural Networks - Free Course
            Description: This free course will help you understand the end-to-end working of neural networks in a simple manner. By the end of this course, you will be able to build advanced Deep Learning models using the PyTorch framework. With a carefully curated list of resources and exercises, this course serves as your comprehensive guide to mastering deep learning. It is recommended that you complete the advanced Machine Learning course before taking up this course.
            Curriculum: How are Neural Networks trained - Forward Propagation | Understanding Loss Functions + Hands on | Reading: Creating a Custom Loss Function (Optional) | Optimization Techniques - Gradient Descent | What is Back Propagation? | Types of Gradient Descent | Common Optimization Techniques - Part 1 | Common Optimization Techniques - Part 2 | Building a Deep Neural Network (Hands-on Regression Model) | Building a Deep Neural Network (Hands-on Classification Model) | Quiz
            Key Takeaways: Utilize leading AI tools like ChatGPT, Microsoft Copilot, and DALL·E3 to create text and image content, enhancing your ability to innovate and streamline your creative processes. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: The A to Z of Unsupervised ML - Free Course
            Description: Unsupervised machine learning helps uncover hidden patterns and structures in data without labeled examples. It is essential for exploratory data analysis, reducing dimensionality, and discovering intrinsic relationships within datasets. Mastering unsupervised techniques enhances data preprocessing and drives insights in complex datasets where labels are scarce or unavailable.
            Curriculum: Resources to be used in this course. | Setting the Context | Choosing Clustering Algorithms | Solving our Problem using k-means - Part 1 | Solving our Problem using k-means - Part 2 | Finding optimal K value | Analysis and Insights Based on the Plots | Introduction to Hierarchical Clustering Analysis (HCA) | Solving our Problem using Hierarchical Clustering | Introduction to DBSCAN Clustering | Solving our Problem using DBSCAN | Reading: Applications of Clustering in the Real World | Project
            Key Takeaways: Learn machine learning techniques and build real-world Unsupervised ML Models. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Building Your first RAG System using LlamaIndex - Free Course
            Description: This course will guide you through building your first Retrieval-Augmented Generation (RAG) system using LlamaIndex. You will start with data ingestion by loading a file into the system, followed by indexing the data for efficient retrieval. Next, you will set up retrieval configurations and use a response synthesizer to combine data into a coherent response. Finally, you will employ a query engine to generate responses. By the end of this course, you will have a solid understanding of these processes and be able to build an RAG system using LlamaIndex code effectively.
            Curriculum: Welcome to this course | Why RAG | What is RAG system | Overview of RAG Framework | Quiz | Course handouts | Introduction to LlamaIndex | Components of LlamaIndex | Reading Material: How to get your API Key | How to get Open AI Keys - 2 min - Website go through | Build Your First RAG system using LlamaIndex | Quiz
            Key Takeaways: Learn the steps involved in building a RAG system using Llamaindex. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Data Preprocessing on a Real-World Problem Statement - Free Course
            Description: This course will help you get a practical understanding of Data Preprocessing. After this course, you can work on any data and prepare it for modelling. With a carefully curated list of resources, this course is your first step to becoming a Data Scientist. By the end of the course, you will have mastered techniques like EDA and Missing Value Treatment.
Who Should Enroll:

Professionals: Individuals looking to expand their skill set on data cleaning and preparation.
Aspiring Students: For those setting out on their journey to become a data scientist and making a mark in the tech world.
            Curriculum: Resources to be used in this course | Introduction to Problem Statement | Reading Material - Understanding the Data | ML-workflow | Tasks to be Performed | Combining Product Attribute Data with POS Data | Combining all the tables in the Dataframe | Understanding the Combined Data | Treating Missing Values - Part 1 | Treating Missing Values Part - 2 | Outlier Detection and Treatment | Preparing the Dataset for Supervised and Unsupervised Models | Generative AI for Data Analysis
            Key Takeaways: Learn data preprocessing tasks | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios
            FAQs: 
            


key: chroma:document
string_value:
            Title: Exploring Stability.AI - Free Course
            Description: This course will give you a practical understanding of Stability.AI tools. By the end of the course, you will be able to deploy and customize SD WebUI, and use the Automatic1111 WebUI on RunPod GPU environments. You'll learn to install, set up, generate, and fine-tune SD WebUI settings, equipping you with the skills to harness Stability.AI's full potential for your projects.

Who Should Enroll:

Professionals: Individuals aiming to enhance their skill set and apply Stability.AI tools/Stable Diffusion in various fields.
Aspiring Students: Those beginning their journey to mastering Generative AI tool deployment and customization, looking to make an impact in the evolving world of Generative AI
            Curriculum: Introduction to Stability | How to use Stability.AI tools | Review of Deployment Options for SD WebUI | Automatic1111 WebUI on RunPod GPU environment | SD WebUI Hands-On - Installation and Setup | SD WebUI Hands-On - Generation and Settings | Quiz
            Key Takeaways: Learn to effectively use and customize SD WebUI and the Automatic1111 WebUI. | Hands-On Experience: Engage in practical exercises to reinforce learning and apply concepts, ensuring you gain the skills to utilize Stability.AI tools confidently.
            FAQs: 
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/free-building-textclassification-natural-language-processing


key: chroma:document
string_value:
            Title: Building a Text Classification Model with Natural Language Processing - Free Course
            Description: Gain practical insights into Natural Language Processing (NLP) with our comprehensive course. Learn to build NLP models using PyTorch, delve into classification models, and apply techniques like bag-of-words, count vectorizer and so on. Perfect for professionals seeking to enhance their skills and aspiring students entering the tech industry.
Who Should Enroll:

Professionals: Expand your skill set with NLP for real-world applications in diverse industries. 
Aspiring Students: Master text data analysis and kickstart your career in AI and NLP.
            Curriculum: What is NLP | Common tasks in a NLP Project | NLP Libraries | Resources for the Course | Methods of Text Preprocessing - Part 1 | Methods of Text Preprocessing - Part 2 | Methods of Text Preprocessing - Part 3 | Quiz | Introduction to dataset and problem statement | Creating a Basic Review Classification Model | Understanding TF-IDF and its implementations | Understanding N-grams | Advanced Preprocessing Techniques | Building an basic ANN model | Limitations of ANN | Quiz
            Key Takeaways: Acquire practical skills in building NLP models using PyTorch | Career Readiness: Prepare to apply NLP across industries, equipping yourself for roles in data science, AI, and text analysis with hands-on exercises.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Getting Started with Large Language Models
            Description: This course will help you gain a comprehensive understanding of Large Language Models (LLMs) and develop advanced natural language processing (NLP) applications using the PyTorch framework. With a carefully curated list of resources and exercises, this course is your guide to becoming an expert in LLMs. Master the techniques to build and fine-tune LLMs, and generate human-like text.
Who Should Enroll: Professionals: Individuals looking to expand their skill set and leverage LLMs across different industries. Aspiring Students: For those setting out on their journey to master language data analysis and leave a mark in the tech world.
            Curriculum: Course Objective | Course Handouts | The Exponential Growth | The Evolution of NLP: Symbolic NLP | The Evolution of NLP: Statistical NLP | The Evolution of NLP: Deep Learning | The Evolution of NLP: Deep Learning Era II | The Evolution of NLP: Tranformers and Evolution | Quiz | Introduction to Large Language Model | What is a Large Language Model? | Understanding Foundational Models | Different types of LLMs: Based on Response | Different types of LLMs: Based on Model Architecture | Quiz | The Current State of the Art in LLMs | Generative AI- Glossary | Your Feedback Matters!
            Key Takeaways: Learn Large Language Model techniques and build real-world NLP applications. | Hands-On Experience: Engage with exercises designed to reinforce your learning and apply concepts in real-world scenarios.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Introduction to Generative  AI
            Description: This course will provide you with a comprehensive understanding of generative AI, including text and image generation techniques. By the end of the course, you will be have an understanding of using generative AI tools to create diverse content. You'll learn how generative AI works, engage in practical exercises, and gain the skills to implement these techniques in real-world projects.
Who Should Enroll:


Professionals: Individuals looking to enhance their skills in generative AI and apply advanced techniques to create innovative solutions across various domains.

Aspiring Students: Individuals eager to enter the field of generative AI and apply generative AI techniques to tackle complex problems and generate creative content across different fields.
            Curriculum: What is Generative AI | How does Gen AI work | Quiz | Text Generation with Gen AI | Image Generation with Gen AI | Quiz | Meet your Instructors | Course Handout | Your Feedback Matters!
            Key Takeaways: Learn to effectively use text and image generation tools to create diverse and innovative content | Engage in practical exercises to reinforce learning and apply generative AI concepts
            FAQs: 
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/nano-course-dreambooth-stable-diffusion-for-custom-images


key: chroma:document
string_value:
            Title: Nano Course: Dreambooth-Stable Diffusion for Custom Images
            Description: Have you ever wondered how to turn your dreams into reality by creating images of your dog traveling around the world or yourself alongside Elon Musk or playing cricket with MSD?
This is exactly where the dreambooth model comes into the picture. With the help of Dreambooth, you can personalize the stable diffusion for a particular subject.
Given just 5 images of our subject, dreambooth can create new images across diverse scenes, poses, views, and lighting conditions that do not appear in the reference images.
In this free nano course on Dreambooth, Sandeep will discuss the historical journey of stable diffusion, its current landscape, and a brief understanding of the stable diffusion training process. Then we will move on to the dreambooth, its training process and finetune dreambooth on our custom dataset.
            Curriculum: The Current Landscape of Generative AI | Why Stable Diffusion | Recap on History of Stable Diffusion | Intuition behind Stable Diffusion | How to train a Stable Diffusion model | Introduction to Dreambooth | Understanding the Dreambooth Process | Tricks to Name Your Concept Uniquely | How to Select Images for Finetuning Dreambooth | Setting up the Training Environment | Code-Finetuning Dreambooth model on Custom Dataset | The Importance of Captioning in Dreambooth | Differences between Stable Diffusion and Dreambooth
            Key Takeaways: Finetune Dreambooth on the custom dataset discussing each step in detail | Understand the training process of dreambooth | Learn Tricks to Name Your Concept Uniquely in dreambooth | Overview of Stable Diffusion, its journey and training process. | Understand the difference between stable diffusion and dreambooth
            FAQs: 
            


key: chroma:document
string_value:
            Title: A Comprehensive Learning Path for Deep Learning in 2023
            Description: The most common question we get from beginners in the field of Deep Learning is - Where to begin? The journey to becoming a Deep Learning expert can be difficult if one does not have the right resources to follow. There are a million resources to refer and it is tough to decide where to start from.
We are here to help you take your first steps into the world of Deep Learning. Here is a free learning path for people who want to become a Deep Learning expert in 2023. We have arranged the best resources in a logical manner along with exercises to make sure that you only need to follow one single source to become a data scientist.
            Curriculum: Getting Started | Overview of the Learning Path | Month-on-Month Plan | Introduction to Deep Learning | Applications of Deep Learning | Setting up your System | Descriptive Statistics and Probability | Python | Exercise : Python | AI&ML Blackbelt Plus Program (Sponsored) | Start engaging in data science / deep learning communities | Inferential Statistics | Exercise : Statistics | Partial Derivative | Linear Algebra - Part 1 | Linear Regression | Logistic Regression | Exercise : Linear and Logistic Regression | Regularization Techniques (Ridge and Lasso) | Project | Start building your GitHub profile | Start building your GitHub profile | Linear Algebra - Part 2 | Getting Started with Neural Networks | Understanding Forward Propagation | Understanding Back Propagation | Exercise : Understanding Neural Networks | Build your first Neural Network in Numpy | Frameworks for Deep Learning | Introduction to Keras | Build your first Neural Network in Keras | Project | Start Participating in Competitions | Handling / Pre-processing Images | Exercise | Hyperparameter Tuning | Regularization Techniques | Optimization Algorithms | Exercise | Project | Understanding Convolutional Neural Networks (CNNs) | Exercise : CNNs | Hyperparameter Tuning | Transfer Learning | Data Augmentation | Project | Build your resume and apply for Internships | Visualizing Convolutional Neural Networks | Project 3 on CV | Project 4 on CV | Start writing articles | Handling / Pre-processing Text Data | Exercise | Recurrent Neural Networks (RNNs) | RNNs - Video | LSTM | GRUs | Transfer Learning for NLP | Project 1 on NLP | Word Embeddings | Exercise | Project 2 on NLP | Attention Models | Attention Models - Text | Project on Attention Models | Unsupervised Deep Learning | Project on Unsupervised Deep Learning | GANs : Video | GANs | Project on GANs | Apply for Jobs and Internships | Way Forward
            Key Takeaways: 
            FAQs: Q: What web browser should I use? A: Our training platform works best with current versions of Chrome, Firefox or Safari, or with Internet Explorer version 9 and above.

See our list of supported browsers for the most up-to-date information. | Q: How much do I need to pay for this course? A: Nothing! Yes - you read it right. This course is free for our community members as a way to get them started in Data Science. | Q: Do I get certificate upon completion of the course? A: No, we do not provide certificate with this course. | Q: Where do I ask my queries? A: You can post your queries on the discussion for the course or share them on the discuss portal at discuss.analyticsvidhya.com
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/a-comprehensive-learning-path-to-become-a-data-scientist-in-twenty-twenty-four


key: chroma:document
string_value:
            Title: A Comprehensive Learning Path to Become a Data Scientist in 2024
            Description: Where do I begin? Data science is such a huge field - where do you even start learning about Data Science?
These are career-defining questions often asked by data science aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.
Don’t worry, we are here to help you take your first steps into the world of data science! Here’s the learning path for people who want to become a data scientist in 2023. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data scientist.
Moreover, we have added the most in-demand skills for the year 2023 for data scientists including storytelling, model deployment, and much more along with exercises and assignments.
            Curriculum: Overview of Learning Path | Month-on-Month Plan | Your Personalized Learning Path for Data Science | AI&ML Blackbelt Plus Program (Sponsored) | Plan for January 2024 | Understanding Machine Learning and its impact | Job of Data Scientist | Exercise | Overview of the Course | A brief introduction to Python | Introduction to Python Test | Installing Python | Theory of Operators | Exercise | Understanding Operators in Python | Operators Test | Understanding variables and data types | Variable Test | Variables and Data Types in Python | Understanding Conditional Statements | Exercise | Implementing Conditional Statements in Python | Conditional Statements test | Understanding Looping Constructs | Exercise | Implementing Looping Constructs in Python | Looping Constructs test | Understanding Functions | Implementing Functions in Python | Functions test | A brief introduction to data structure | Data Structure test | Understanding the concept of Lists | Lists test | Implementing Lists in Python | Exercise | Understanding the concept of Dictionaries | Exercise | Implementing Dictionaries in Python | Dictionaries test | Understanding the concept of Standard Libraries | Libraries test | Reading a CSV File in Python - Introduction to Pandas | Reading a CSV file in Python: Implementation | Reading a csv file in Python test | Understanding dataframes and basic operations | DataFrames and basic operations test | Reading dataframes and conduct basic operations in Python | Reading dataframes and conduct basic operations in Python Test | Indexing a Dataframe | Indexing DataFrames test | Exercise | Sorting Dataframes | Merging Dataframes | Quiz: Sorting and Merging dataframes | Apply function | Aggregating data | Quiz: Apply function and Aggregating data | Basics of Matplotlib | Data Visualization using Matplotlib | Quiz: Matplotlib | Basics of Seaborn | Data Visualization using Seaborn | Quiz: Seaborn | Regular Expressions | Understanding Regular Expressions | Quiz: Regular Expressions | Regular Expressions in  Python | Quiz: Regular Expressions in Python | Cheatsheet for Python | Instructions | Quiz | Python Coding Challenge | Exercise - Definition of Data Visualization | Why Tableau is a Powerful Tool for Professionals | Compare Tableau Against Power BI and Qlik | The Tableau Range of Products | The 5 Tableau Products you should Know | Installing Tableau Desktop on your System | Installing Tableau Public on your System | Difference Between Tableau Server and Tableau Online | Navigating the Tableau Interface (Part 1) | Navigating the Tableau Interface (Part 2) | Connecting to Data Sources in Tableau | Understanding the Problem Statement | Download the Superstore Dataset | Loading the Dataset and Getting Familiar with the Variables | Build your First Visualization in Tableau! | Hands-On with Labels and Formatting | Playing Around with Colors | Using Filters to Build a Pivot Structure in Tableau | Exporting your Tableau Worksheet | The Different Chart Types in Tableau | Line Charts - Working with Time Series Data | Building Line Charts in Tableau | Exercise - Sales of Each Category Month-by-Month | Generating Map Visualizations for Geospatial Analysis | Map Visualizations in Tableau | Exercise - Sales by City Analysis | Bar Charts, Histograms, Scatter Plots, Bubble Charts, Pie Charts | Dual Axis Charts in Tableau | Date Dual Axis Charts in Tableau | What are Calculated Fields? | Feature Engineering in Tableau - Average Shipping Time | Exercise - Number of Orders per State | Calculating the Average Order Value | Average Order Value for Product Sub-Categories | What are Parameters in Tableau? | Using Parameters to find Top N Customers | Using Parameters to Analyze Superstore's Variable Values | Joins and their Different Types in Tableau | Performing Data Joining in Tableau | What is Blending? How is it Different from Joins? | Blending Data in Tableau | Download the Coffee Chain Dataset | Introduction to Dashboards and their Use Cases | Reading Material - Dashboards in Tableau | Designing your First Dashboard in Tableau | Using Parameters to Create Dynamic Dashboards | How to Upload your Work to the Tableau Public Gallery | Designing the Blueprint for a Multi-Dashboard View to Analyze Sales | Building Multiple Interlinked Dashboards in Tableau for our Business | The Art of Storytelling | 3-Step Storytelling Framework | Sketching the Story Blueprint | Profits by Region Analysis using Storyboard in Tableau | Capstone Project: Sales and Profit by Segment using Storyboards in Tableau | Getting started with SQL | Types of Databases | How data is Stored in Relational Databases | How data is stored in NoSQL databases | Exercise 1 | Introduction | Architecture: Client and Server | MySQL Distributions | Local Installation on Mac | Local Installation on Linux | Local Installation on Windows | Licensing | Accessing a remote MySQL server | Graphical user interfaces | Exercise 2 | SQL - Installation Guide | Introduction | What exactly is SQL? | History of SQL | Connecting to MySQL | Types of Commands - DDL (Creation/ Deletion/ Updating of Schema | Types of Commands - DML (Manipulating data in tables) | Types of Commands - DCL (Managing Access control) | Exploring databases | Creating tables | Inserting data in tables | SELECT Statement - Introduction | Datatypes in MySQL | NULL vs NOT NULL | Exercise 3 | Introduction | Update command – Concept | Update command – Example | Delete command – Concept | Delete command – Example | Describe command – Concept | Describe command – Example | Alter command – Concept and Example | Exercise 4 | Introduction | Importing data from CSV to MySQL | Exporting data from MySQL to CSV | Backing up databases | Restoring databases | Exercise 5 | Importing and Exporting Datasets - Troubleshooting Guide | Introduction | Counting Rows and Items | Aggregation Functions – SUM, AVG, STDDEV | Extreme Values Identification – MIN, MAX | Slicing data | Limiting data | Sorting data | Filtering Patterns | Groupings, Rolling up data and Filtering in Groups | Exercise 6 | Introduction | Data Eyeballing | Data Dictionary | Questions we need answers of | Analyzing data and creating table structure | Loading data to our MySQL table | Data Analysis – Simple Queries | Data Analysis – Advanced Queries | FIFA19 Players dataset (cleaned) for this Project | Introduction | The need for joins | Different type of joins | The Left Join - Concept | The Left Join – Practical Example | The Inner Join | The Cross Join | The Right Join | The Self Join | Exercise | Introduction | Introduction to Indexing | How indexing works (basics) | Relationships | Types of Relationships | Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT | Exercise | String functions - CONCAT | String functions – Case Conversion | String functions – Trimming Strings | String functions – Extracting Substrings | Date/ Time functions – Current date and time | Date/ Time functions – Extracting date and time from field | Date/ Time functions – Formatting date and time as Strings | Numeric functions | SQL CheatSheet | Exercise | Introduction | Setting up a virtual environment | Installing the required packages | Connecting to MySQL | Connecting to database table and pulling data | Querying the database- INSERT | Querying the database- DELETE | Querying the database- SEARCH | Querying the database- INDEXING | Notes and Resources | Subscribe to Data Science Newsletter and Podcast | Overview of Statistics | Important applications of Statistics | What is Descriptive Statistics? | Introduction to Design experiments | Introduction to Design experiments-Video | Exercise | Visualizing Data | Visualizing Data | Central tendency | Exercise | Variability | Unimodal Distribution of Data | Bimodal Distribution of Data | Normal distribution – Part 1 | Normal distribution – Part 2 | Z-Score | Introduction to Probability- An Overview | Principal Of Counting | Exercise | Permutation | Exercise | Combination | Exercise | Conditional Probability – Part 1 | Conditional Probability – Part 2 | Exercise | Binomial Distribution | Random variable | Expectation and variance | Exercise | Cheatsheet for Probability | Statistics: Inferential-Hypothesis Testing | T-test | One Way ANOVA | Chi-square | Cheatsheet on Statistics | Exploratory Data Analysis (EDA)- Data Exploration | Cheatsheet on EDA | Project-1 | Loan Prediction | Project-2 | Big Mart Sales | Linear Algebra | Free Course | Overview of Machine Learning | Understanding Data Science Pipeline | Get Familiarised with Command Line (Linux)- Guide | Linear Regression | Linear Regression-Video | Exercise | Logistic Regression- Part 1 | Logistic Regression – Part 2 | Exercise | Decision Tree Algorithm | Exercise | Naive Bayes | Support Vector Machine | Regression Project - Big Mart Sales | Classification Project - Loan Prediction | Introduction to Structured Thinking | Commonly Asked Puzzles in Interviews | How to solve Guesstimates? | Excercise: Strategic Thinking | Structured Thinking and Communication Course | Ensemble Learning Basics | Ensemble Learning Basics-Video | Bagging | Boosting | Random Forest - Simplified | Random Forest - Detailed with implementation | Exercise | Boosting - Detailed with implementation | XGBoost | LightGBM | CatBoost | Exercise | Advanced Ensemble Technique - Blending | Advanced Ensemble Learning - Stacking | Cheatsheet for Machine Learning | Image data | Text data | Audio data | Audio data-Video | Projects | Participating in Competitions | Introduction to validation | Different Types of Validation Techniques | K-fold Cross Validation - Implementation | Summary - Validation Techniques | Exercise | Different methods for finding best hyperparameters of an algorithm | Hyperparameter tuning for Random Forest | Hyperparameter tuning for GBM | Hyperparameter tuning for XGBoost | Hyperparameter tuning for LightGBM | Bayesian Hyperparameter Optimization | Feature Engineering | Profile Building | Building your Resume | Up Level your Data Science Resume Course | Ace Data Science Interview Course | Basics of Matrix Algebra | Matrix Calculus | Dimensionality Reduction - Overview | Principal Component Analysis (PCA) | Singular Value Decomposition (SVD) | Singular Value Decomposition (SVD)-Text | Unsupervised Learning-K Means and Hierarchical Clustering | Clustering - Project | Learn Github | Introduction to Recommendation Systems | Introduction to Recommendation Systems - Video | Project: Recommendation System | Implementation in Python | Introduction to Time Series Forecasting | Handling a Non-Stationary Time Series in Python | Time Series Modeling using ARIMA | Time Series Modeling using Prophet Library | Time Series Project | Project - Black Friday | Setting up the System for Deep Learning | Introduction to Deep Learning | Build your first Neural Network in Numpy | Why are GPUs necessary for Deep Learning? | The Evolution and Core Concepts of Deep Learning & Neural Networks | An Introduction to Implementing Neural Networks using TensorFlow | Introduction to Keras | Optimizing Neural Networks using Keras (with Image recognition case study) | Understanding Convolutional Neural Networks (CNNs) | Build Image Classification Model using Keras | Exercise | Transfer Learning for Computer Vision | Computer Vision Project 1 : Identify the Apparels | Computer Vision Project 2: Scene Classification | Computer Vision using Deep Learning Course | Computer Vision Course (Sponsored) | Cheatsheet for Keras | Write for Analytics Vidhya's Medium Publication | Recurrent Neural Network | Long short Term Memory Networks (LSTM) | Gated Recurrent Unit (GRU) | Useful resources-GRU | Text Preprocessing | Text Cleaning | Text Classification | Natural Language Processing (NLP) Using Python Course | How to Deploy Machine Learning Models using Flask | Tutorial to deploy Machine Learning models in Production as APIs | Deploying machine learning models using Streamlit – An introductory guide to Model Deployment | An Ode to Model Deployment using Streamlit – Open Sourcing “Typing Tutor for Programmers” | Building a Portfolio with Projects
            Key Takeaways: Beginner friendly course: This is a beginner-friendly course and has no prerequisites. | Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources and hackathons. | Updated skillset for 2023: The knowledge of Machine Learning models is important but that won’t set you apart. We have included some of the top unique skills you’ll require to become a data scientist in 2023. | Assignments to test yourself: What’s the best way to test your knowledge? Each module comes with assignments and MCQs to give your memory a boost.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Nano Course: Building Large Language Models for Code
            Description: In this Free Nano GenAI Course on Building Large Language Models for Code, you will-

Learn how to train LLMs for Code from Scratch covering Training Data Curation, Data Preparation, Model Architecture, Training, and Evaluation Frameworks.
Explore each step in-depth, delving into the algorithms and techniques used to create StarCoder, a 15B code generation model trained on 80+ programming languages.
Understand and learn the best practices to train your own StarCoder on the data
            Curriculum: Introduction | Agenda | BigCode Community | Training LLMs for Code from Scratch: Training Data Curation | Training Data Formatting and Preprocessing | Model Architecture | BigCode Ecosystem | Training Frameworks | Model Evaluation | Tools and Descendants of StarCoder
            Key Takeaways: Learn how to train LLMs for code fom scratch | Deep dive into StarCoder journey | Understand algorithms and techniques used at each step involved in development of StarCoder | Learn best practices to train your own StarCoder model on data | Explore  the model architecture, training and evaluation frameworks for Code LLMs
            FAQs: 
            


key: chroma:document
string_value:
            Title: Machine Learning Summer Training
            Description: This is the second step of the Machine Learning Summer Training, want to know more click here.
            Curriculum: AI&ML Blackbelt Plus Program (Sponsored) | Overview of Machine Learning / Data Science | Common Terminology used in Data Science | Applications of Data Science | Installation steps for Windows | Installation steps for Linux | Installation steps for Mac | Introduction to Python | Introduction to Jupyter Notebook | Download Python Module Handouts | Introduction to Variables | Implementing Variables in Python | Introduction to Operators | Implementing Operators in Python | Quiz: Operators | Introduction to Conditional Statements | Implementing Conditional Statements in Python | Quiz: Conditional Statements | Introduction to Looping Constructs | Implementing Loops in Python | Quiz: Loops in Python | Break, Continue and Pass Statements | Quiz: Break, Continue and Pass Statement | Introduction to Data Structures | List and Tuple | Implementing List in Pyhton | Quiz: Lists | List - Project in Python | Implementing Tuple in Python | Quiz: Tuple | Introduction to Sets | Implementing Sets in Python | Quiz: Sets | Introduction to Dictionary | Implementing Dictionary in Python | Quiz: Dictionary | Introduction to String Manipulation | Quiz: String Manipulation | Introduction to Functions | Implementing Functions in Python | Quiz: Functions in Python | Lambda Expression | Quiz: Lambda Expressions | Recursion | Implementing Recursion in Python | Quiz: Recursion | Introduction to Modules | Modules: Intuition | Introduction to Packages | Standard Libraries in Python | User Defined Libraries in Python | Quiz:  Modules, Packages and Standard Libraries | Handling Text Files in Python | Quiz: Handling Text Files | Important Libraries for Data Science | Quiz: Important Libraries for Data Science | Basics of Numpy in Python | Basics of Scipy in Python | Quiz: Numpy and Scipy | Basics of Pandas in Python | Quiz: Pandas | Basics of Matplotlib in Python | Basics of Scikit-Learn in Python | Basics of Statsmodels in Python | Reading Data in Python | Reading CSV files in Python | Reading Big CSV Files in Python | Quiz: Reading CSV files in Python | Reading Excel & Spreadsheet files in Python | Quiz: Reading Excel & Spreadsheet files in Python | Reading JSON files in Python | Quiz: Reading JSON files in Python | Subsetting and Modifying Data in Python | Overview of Subsetting in Pandas I | Overview of Subsetting in Pandas II | Subsetting based on Position | Subsetting based on Label | Subsetting based on Value | Quiz: Subsetting Dataframes | Modifying data in Pandas | Quiz: Modifying Dataframes | Preprocessing, Sorting and Aggregating Data | Sorting the Dataframe | Quiz:  Sorting Dataframes | Concatenating Dataframes in Pandas | Concept of SQL-Like Joins in Pandas | Implementing SQL-Like Joins in Pandas | Quiz: Joins in Pandas | Aggregating and Summarizing Dataframes | Preprocessing Timeseries Data | Quiz: Preprocessing Timeseries Data | Visualizing Trends & Pattern in Data | Basics of Matplotlib | Data Visualization with Matplotlib | Quiz: Matplotlib | Basics of Seaborn | Data Visualization with Seaborn | Quiz: Seaborn | 6 Steps of Machine Learning Lifecycle | Introduction to Predictive Modeling | Defining the Problem statement | Introduction to Hypothesis Generation | Performing Hypothesis generation | Quiz - Performing Hypothesis generation | List of hypothesis | Data Collection/Extraction | Quiz - Data Collection/Extraction | Introduction to Exploratory Data Analysis & Data Insights | Quiz -  Introduction to Exploratory Data Analysis & Data Insights | Role of Statistics in EDA | Descriptive Statistics | Inferential Statistics | Quiz - Descriptive and Inferential Statistics | Quiz: Build a Benchmark Model - Regression | Benchmark Model: Regression Implementation | Quiz: Benchmark Model - Regression Implementation | Build a Benchmark Model: Classification | Quiz: Build a Benchmark Model - Classification | Benchmark Model: Classification Implementation | Quiz: Benchmark - Classification Implementation | Introduction to Evaluation Metrics | Quiz: Introduction to Evaluation Metrics | Confusion Matrix | Quiz: Confusion Matrix | Accuracy | Quiz: Accuracy | Alternatives of Accuracy | Quiz: Alternatives of Accuracy | Precision and Recall | Quiz: Precision and Recall | Thresholding | Quiz: Thresholding | AUC-ROC | Quiz: AUC-ROC | Log loss | Quiz: Log loss | Evaluation Metrics for Regression | Quiz: Evaluation Metrics for Regression | R2 and Adjusted R2 | Quiz: R2 and Adjusted R2 | Dealing with Missing Values in the Data | Quiz: Dealing with missing values in the data | Replacing Missing Values | Quiz: Replacing Missing values | Imputing Missing Values in data | Quiz: Imputing Missing values in data | Working with Categorical Variables | Quiz: Working with categorical data | Working with Outliers | Quiz: Working with outliers | Preprocessing Data for Model Building | Building a kNN model | Quiz: Building a kNN model | Determining right value of k | Quiz: Determining right value of k | How to calculate the distance | Quiz: How to calculate the distance | Issue with distance based algorithms | Quiz: Issue with distance based algorithms | Introduction to sklearn | Implementing k-Nearest Neighbours algorithm | Quiz: Implementing k-Nearest Neighbours algorithm | Introduction to Overfitting and Underfitting Models | Quiz: Introduction to Overfitting and Underfitting Models | Visualizing overfitting and underfitting using knn | Quiz: Visualizing overfitting and underfitting using knn | Selecting the Right Model | What is Validation? | Quiz: What is Validation | Understanding Hold-Out Validation | Quiz: Understanding Hold-Out Validation | Implementing Hold-Out Validation | Quiz: Implementing Hold-Out Validation | Understanding k-fold Cross Validation | Implementing k-fold Cross Validation | Quiz: Understanding k-fold Cross Validation | Quiz: Implementing k-fold Cross Validation | Bias Variance Tradeoff | Quiz: Bias Variance Tradeoff | Introduction to Linear Models | Quiz: Introduction to linear model | Understanding Cost function | Quiz: Understanding Cost function | Understanding Gradient descent (Intuition) | Maths behind gradient descent | Convexity of cost function | Quiz: Convexity of Cost function | Quiz: Gradient Descent | Assumptions of Linear Regression | Quiz: Assumptions of linear model | Implementing Linear Regression | Generalized Linear Models | Quiz: Generalized Linear Models | Introduction to Logistic Regression | Quiz: Introduction to logistic regression | Quiz: Logistic Regression | Odds Ratio | Implementing Logistic Regression | Multiclass using Logistic Regression | Quiz: Multi-Class Logistic Regression | Challenges with Linear Regression | Quiz: Challenges with Linear regression | Introduction to Regularisation | Quiz: Introduction to Regularization | Implementing Regularisation | Coefficient estimate for ridge and lasso (Optional) | Predicting whether a customer will churn or not | Introduction to Decision Trees | Quiz: Introduction to Decision Trees | Purity in Decision Trees | Quiz: Purity in Decision Trees | Terminologies Related to Decision Trees | Quiz: Terminologies Related to Decision Trees | How to Select the Best Split Point in Decision Trees | Quiz: How to Select the Best Split Point in Decision Trees | Chi-Square | Quiz: Chi-Square | Information Gain | Quiz: Information Gain | Reduction in Variance | Quiz: Reduction in Variance | Optimizing Performance of Decision Trees | Quiz: Optimizing Performance of Decision Trees | Decision Tree Implementation | Introduction to Feature Engineering | Quiz: Introduction to feature engineering | Exercise on Feature Engineering | Overview of the module | Feature Transformation | Quiz: Feature Transformation | Feature Scaling | Quiz: Feature Scaling | Feature Encoding | Quiz: Feature Encoding | Combining Sparse classes | Quiz: Combining Sparse classes | Feature Generation: Binning | Quiz: Feature Generation- Binning | Feature Interaction | Quiz: Feature Interaction | Generating Features: Missing Values | Frequency Encoding | Quiz: Frequency Encoding | Feature Engineering: Date Time Features | Implementing DateTime Features | Quiz: Implementing DateTime Features | Automated Feature Engineering : Feature Tools | Implementing Feature tools | Quiz: Implementing Feature Tools | Exploring the NYC dataset | Predicting the NYC taxi trip duration | Predicting the NYC taxi trip duration | Share Your Feedback about the course. | Would  you recommend this course to your Friends.
            Key Takeaways: Python libraries like Numpy, Pandas, etc. to analyze your data efficiently. | Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field. | Linear Regression, Logistic Regression, and Decision Trees for building machine learning models. | Understand how to solve Classification and Regression problems using machine learning | How to evaluate your machine learning models using the right evaluation metrics? | Improve and enhance your machine learning model’s accuracy through feature engineering
            FAQs: Q: Who should take the Free Machine Learning Certification Course for Beginners? A: This course is meant for people looking to learn Machine Learning. We will start with understanding Python for Data Science, the importance of statistics and EDA, the underlying intuition behind several machine learning algorithms and then go on to solve case studies using Machine Learning concepts. | Q: Do I need to install any software before starting the course? A: You will get information about all installations as part of the course. | Q: Do I need to take the modules in a specific order? A: It is highly recommended to take the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get a machine learning certificate upon completion of the course? A: Yes, you will be given a certificate upon satisfactory completion of the Free Machine Learning Certification Course for Beginners.
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/a-comprehensive-learning-path-to-become-a-data-engineer-in-2022


key: chroma:document
string_value:
            Title: A Comprehensive Learning Path to Become a Data Engineer in 2022
            Description: Where do I begin? Data Engineering is such a huge field - where do you even start learning about Data Engineering?
These are career-defining questions often asked by data engineering aspirants. There are a million resources out there to refer but the learning journey can be quite exhausting if you don’t know where to start.
Don’t worry, we are here to help you take your first steps into the world of data engineering! Here’s the learning path for people who want to become a data engineer in 2022. We have arranged and compiled all the best resources in a structured manner so that you have a unified resource to become a successful data engineer.
Moreover, we have added the most in-demand skills for the year 2022 for data engineers including storytelling, model deployment, and much more along with exercises and assignments.
            Curriculum: Overview of Learning Path | Month-on-Month Plan | AI&ML Blackbelt Plus Program (Sponsored) | Overview of the Course | A brief introduction to Python | Introduction to Python Test | Installing Python | Become a BlackBelt in Data Science | Theory of Operators | Exercise | Understanding Operators in Python | Operators Test | Understanding variables and data types | Variable Test | Variables and Data Types in Python | Exercise | Understanding Conditional Statements | Exercise | Implementing Conditional Statements in Python | Conditional Statements test | Understanding Looping Constructs | Exercise | Implementing Looping Constructs in Python | Looping Constructs test | Understanding Functions | Implementing Functions in Python | Functions test | A brief introduction to data structure | Data Structure test | Understanding the concept of Lists | Lists test | Implementing Lists in Python | Exercise | Understanding the concept of Dictionaries | Exercise | Implementing Dictionaries in Python | Dictionaries test | Understanding the concept of Standard Libraries | Libraries test | Reading a CSV File in Python - Introduction to Pandas | Reading a CSV file in Python: Implementation | Reading a csv file in Python test | Understanding dataframes and basic operations | DataFrames and basic operations test | Reading dataframes and conduct basic operations in Python | Reading dataframes and conduct basic operations in Python Test | Indexing a Dataframe | Indexing DataFrames test | Exercise | Sorting Dataframes | Merging Dataframes | Quiz: Sorting and Merging dataframes | Apply function | Aggregating data | Quiz: Apply function and Aggregating data | Basics of Matplotlib | Data Visualization using Matplotlib | Quiz: Matplotlib | Basics of Seaborn | Data Visualization using Seaborn | Quiz: Seaborn | Regular Expressions | Understanding Regular Expressions | Quiz: Regular Expressions | Regular Expressions in  Python | Quiz: Regular Expressions in Python | Cheatsheet for Python | Instructions | Quiz | Python Coding Challenge | Test your Skills: Python | Poll | Where to go from here? | Plan for February 2022 | 1.5 Types of Databases | 1.6	How data is Stored in Relational Databases | 1.7	How data is stored in NoSQL databases | Exercise 1 | Course Handouts | 2.1	Introduction | 2.2	Architecture: Client and Server | 2.3	MySQL Distributions | 2.4	Local Installation on Mac | 2.5	Local Installation on Linux | 2.6	Local Installation on Windows | 2.7	Licensing | 2.8	Accessing a remote MySQL server | 2.9	Graphical user interfaces | Exercise 2 | SQL - Installation Guide | 3.1	Introduction | 3.2	What exactly is SQL? | 3.3	History of SQL | 3.4	Connecting to MySQL | 3.5	Types of Commands - DDL (Creation/ Deletion/ Updating of Schema | 3.6	Types of Commands - DML (Manipulating data in tables) | 3.7	Types of Commands - DCL (Managing Access control) | 3.8	Exploring databases | 3.9	Creating tables | 3.10 Inserting data in tables | 3.11 SELECT Statement - Introduction | 3.12 Datatypes in MySQL | 3.13 NULL vs NOT NULL | Exercise 3 | 4.1	Introduction | 4.2	Update command – Concept | 4.3	Update command – Example | 4.4	Delete command – Concept | 4.5	Delete command – Example | 4.6	Describe command – Concept | 4.7	Describe command – Example | 4.8	Alter command – Concept and Example | Copy of Exercise 4 | 5.1	Introduction | 5.2	Importing data from CSV to MySQL | 5.3	Exporting data from MySQL to CSV | 5.4	Backing up databases | 5.5	Restoring databases | Exercise 5 | Importing and Exporting Datasets - Troubleshooting Guide | 6.1	Introduction | 6.2	Counting Rows and Items | 6.3	Aggregation Functions – SUM, AVG, STDDEV | 6.4	Extreme Values Identification – MIN, MAX | 6.5	Slicing data | 6.6	Limiting data | 6.7	Sorting data | 6.8	Filtering Patterns | 6.9	Groupings, Rolling up data and Filtering in Groups | Exercise 6 | 7.1	Introduction | 7.2	Data Eyeballing | 7.3	Data Dictionary | 7.4	Questions we need answers of | 7.5	Analyzing data and creating table structure | 7.6	 Loading data to our MySQL table | 7.7	Data Analysis – Simple Queries | 7.8	Data Analysis – Advanced Queries | FIFA19 Players dataset (cleaned) for this Project | 8.1 Introduction | 8.2.	The need for joins | 8.3.	Different type of joins | 8.4.	The Left Join - Concept | 8.5.	The Left Join – Practical Example | 8.6.	The Inner Join | 8.7.	The Cross Join | 8.8.	The Right Join | 8.9.	The Self Join | Assignment: Share your learning and build your profile | Exercise | 9.1.	 Introduction | 9.2.	 Introduction to Indexing | 9.3.	How indexing works (basics) | 9.4.	Relationships | 9.5.	Types of Relationships | 9.6.	Table Constraints – PRIMARY KEY, FOREIGN KEY, UNIQUENESS and AUTO INCREMENT | Exercise | 10.1 String functions - CONCAT | 10.2 String functions – Case Conversion | 10.3 String functions – Trimming Strings | 10.4 String functions – Extracting Substrings | 10.5 Date/ Time functions – Current date and time | 10.6 Date/ Time functions – Extracting date and time from field | 10.7 Date/ Time functions – Formatting date and time as Strings | 10.8 Numeric functions | SQL CheatSheet | Exercise | 11.1 Introduction | 11.2 Setting up a virtual environment | 11.3 Installing the required packages | 11.4 Connecting to MySQL | 11.5 Connecting to database table and pulling data | 11.6 Querying the database- INSERT | 11.7 Querying the database- DELETE | 11.8 Querying the database- SEARCH | 11.9 Querying the database- INDEXING | 11.10 Notes and Resources | Exercise | Basic Linux Commands | Introduction to Cloud Computing | Cloud Deployment Models | Service Models | Resources: Learn about AWS | Creating Databases and Collections | Inserting Documents | Reading Documents | The _id Field | Importing and Exporting Data | Backup and Restore MongoDB Databases | Updating Documents | Deleting Documents, Collections and Databases | CRUD Operations in MongoDB Atlas | Importing, Exporting and Working with MongoDB Atlas | What is Big Data? | Challenges with Big Data | Applications of Big Data | Distributed Systems | What is Hive | Features of Hive | Working of Hive
            Key Takeaways: Beginner friendly course: The course assumes no prerequisites and is meant for beginners | Curated list of resources to follow: All the necessary topics are covered in the course, in an orderly manner with links to relevant resources. | Updated skillset for 2022: The knowledge of Data Engineering is important but that won’t set you apart. We have included some of the top unique skills you’ll require to become a data engineer in 2022.
            FAQs: 
            


key: chroma:document
string_value:
            Title: Writing Powerful Data Science Articles
            Description: "Either write something worth reading or do something worth writing." - Benjamin Franklin
The best way to learn any concept, especially in data science, is by writing about it. That not only helps you understand what you learned in more detail, but sharing it with the community helps others understand how a particular data science idea works.
But here’s the thing - most people want to write, but just can’t get past the initial challenges. This might sound familiar to a lot of people:

What should I write about?
Will anyone read my article?
How do I make my article stand out?
Should I even write?


If you’ve ever asked yourself these questions, you’ll find the answers in this free crash course on how to write impactful and awesome data science articles!
            Curriculum: Why did we create this course? | AI&ML Blackbelt Plus Program (Sponsored) | Session #1 - How to Write Powerful and Impactful Data Science Articles | Session #2 - Writing Data Science Articles that Grab your Reader's Attention | Expert Talk: Parul Pandey on Writing Powerful Data Science Articles | Congrats! Here's what's next
            Key Takeaways: 
            FAQs: 
            


key: chroma:document
string_value:
            Title: Machine Learning Certification Course for Beginners
            Description: Machine Learning is the science of teaching machines how to learn by themselves. Machine Learning is reshaping and revolutionizing the world and disrupting industries and job functions globally. 
Machine learning is so extensive that you probably use it numerous times a day without knowing it. From unlocking your mobile phones using your face to giving your attendance using a biometric machine, machine learning is being used in almost every stage. 
In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them to real-world business problems.
            Curriculum: AI&ML Blackbelt Plus Program (Sponsored) | Overview of Machine Learning / Data Science | Common Terminology used in Data Science | Applications of Data Science | Installation steps for Windows | Installation steps for Linux | Installation steps for Mac | Introduction to Python | Introduction to Jupyter Notebook | Download Python Module Handouts | Introduction to Variables | Implementing Variables in Python | Introduction to Operators | Implementing Operators in Python | Quiz: Operators | Introduction to Conditional Statements | Implementing Conditional Statements in Python | Quiz: Conditional Statements | Introduction to Looping Constructs | Implementing Loops in Python | Quiz: Loops in Python | Break, Continue and Pass Statements | Quiz: Break, Continue and Pass Statement | Introduction to Data Structures | List and Tuple | Implementing List in Pyhton | Quiz: Lists | List - Project in Python | Implementing Tuple in Python | Quiz: Tuple | Introduction to Sets | Implementing Sets in Python | Quiz: Sets | Introduction to Dictionary | Implementing Dictionary in Python | Quiz: Dictionary | Introduction to String Manipulation | Quiz: String Manipulation | Introduction to Functions | Implementing Functions in Python | Quiz: Functions in Python | Lambda Expression | Quiz: Lambda Expressions | Recursion | Implementing Recursion in Python | Quiz: Recursion | Introduction to Modules | Modules: Intuition | Introduction to Packages | Standard Libraries in Python | User Defined Libraries in Python | Quiz:  Modules, Packages and Standard Libraries | Handling Text Files in Python | Quiz: Handling Text Files | Important Libraries for Data Science | Quiz: Important Libraries for Data Science | Basics of Numpy in Python | Basics of Scipy in Python | Quiz: Numpy and Scipy | Basics of Pandas in Python | Quiz: Pandas | Basics of Matplotlib in Python | Basics of Scikit-Learn in Python | Basics of Statsmodels in Python | Reading Data in Python | Reading CSV files in Python | Reading Big CSV Files in Python | Quiz: Reading CSV files in Python | Reading Excel & Spreadsheet files in Python | Quiz: Reading Excel & Spreadsheet files in Python | Reading JSON files in Python | Quiz: Reading JSON files in Python | Subsetting and Modifying Data in Python | Overview of Subsetting in Pandas I | Overview of Subsetting in Pandas II | Subsetting based on Position | Subsetting based on Label | Subsetting based on Value | Quiz: Subsetting Dataframes | Modifying data in Pandas | Quiz: Modifying Dataframes | Preprocessing, Sorting and Aggregating Data | Sorting the Dataframe | Quiz:  Sorting Dataframes | Concatenating Dataframes in Pandas | Concept of SQL-Like Joins in Pandas | Implementing SQL-Like Joins in Pandas | Quiz: Joins in Pandas | Aggregating and Summarizing Dataframes | Preprocessing Timeseries Data | Quiz: Preprocessing Timeseries Data | Visualizing Trends & Pattern in Data | Basics of Matplotlib | Data Visualization with Matplotlib | Quiz: Matplotlib | Basics of Seaborn | Data Visualization with Seaborn | Quiz: Seaborn | 6 Steps of Machine Learning Lifecycle | Introduction to Predictive Modeling | Defining the Problem statement | Introduction to Hypothesis Generation | Performing Hypothesis generation | Quiz - Performing Hypothesis generation | List of hypothesis | Data Collection/Extraction | Quiz - Data Collection/Extraction | Introduction to Exploratory Data Analysis & Data Insights | Quiz -  Introduction to Exploratory Data Analysis & Data Insights | Role of Statistics in EDA | Descriptive Statistics | Inferential Statistics | Quiz - Descriptive and Inferential Statistics | Quiz: Build a Benchmark Model - Regression | Benchmark Model: Regression Implementation | Quiz: Benchmark Model - Regression Implementation | Build a Benchmark Model: Classification | Quiz: Build a Benchmark Model - Classification | Benchmark Model: Classification Implementation | Quiz: Benchmark - Classification Implementation | Introduction to Evaluation Metrics | Quiz: Introduction to Evaluation Metrics | Confusion Matrix | Quiz: Confusion Matrix | Accuracy | Quiz: Accuracy | Alternatives of Accuracy | Quiz: Alternatives of Accuracy | Precision and Recall | Quiz: Precision and Recall | Thresholding | Quiz: Thresholding | AUC-ROC | Quiz: AUC-ROC | Log loss | Quiz: Log loss | Evaluation Metrics for Regression | Quiz: Evaluation Metrics for Regression | R2 and Adjusted R2 | Quiz: R2 and Adjusted R2 | Dealing with Missing Values in the Data | Quiz: Dealing with missing values in the data | Replacing Missing Values | Quiz: Replacing Missing values | Imputing Missing Values in data | Quiz: Imputing Missing values in data | Working with Categorical Variables | Quiz: Working with categorical data | Working with Outliers | Quiz: Working with outliers | Preprocessing Data for Model Building | Building a kNN model | Quiz: Building a kNN model | Determining right value of k | Quiz: Determining right value of k | How to calculate the distance | Quiz: How to calculate the distance | Issue with distance based algorithms | Quiz: Issue with distance based algorithms | Introduction to sklearn | Implementing k-Nearest Neighbours algorithm | Quiz: Implementing k-Nearest Neighbours algorithm | Introduction to Overfitting and Underfitting Models | Quiz: Introduction to Overfitting and Underfitting Models | Visualizing overfitting and underfitting using knn | Quiz: Visualizing overfitting and underfitting using knn | Selecting the Right Model | What is Validation? | Quiz: What is Validation | Understanding Hold-Out Validation | Quiz: Understanding Hold-Out Validation | Implementing Hold-Out Validation | Quiz: Implementing Hold-Out Validation | Understanding k-fold Cross Validation | Implementing k-fold Cross Validation | Quiz: Understanding k-fold Cross Validation | Quiz: Implementing k-fold Cross Validation | Bias Variance Tradeoff | Quiz: Bias Variance Tradeoff | Introduction to Linear Models | Quiz: Introduction to linear model | Understanding Cost function | Quiz: Understanding Cost function | Understanding Gradient descent (Intuition) | Maths behind gradient descent | Convexity of cost function | Quiz: Convexity of Cost function | Quiz: Gradient Descent | Assumptions of Linear Regression | Quiz: Assumptions of linear model | Implementing Linear Regression | Generalized Linear Models | Quiz: Generalized Linear Models | Introduction to Logistic Regression | Quiz: Introduction to logistic regression | Quiz: Logistic Regression | Odds Ratio | Implementing Logistic Regression | Multiclass using Logistic Regression | Quiz: Multi-Class Logistic Regression | Challenges with Linear Regression | Quiz: Challenges with Linear regression | Introduction to Regularisation | Quiz: Introduction to Regularization | Implementing Regularisation | Coefficient estimate for ridge and lasso (Optional) | Predicting whether a customer will churn or not | Introduction to Decision Trees | Quiz: Introduction to Decision Trees | Purity in Decision Trees | Quiz: Purity in Decision Trees | Terminologies Related to Decision Trees | Quiz: Terminologies Related to Decision Trees | How to Select the Best Split Point in Decision Trees | Quiz: How to Select the Best Split Point in Decision Trees | Chi-Square | Quiz: Chi-Square | Information Gain | Quiz: Information Gain | Reduction in Variance | Quiz: Reduction in Variance | Optimizing Performance of Decision Trees | Quiz: Optimizing Performance of Decision Trees | Decision Tree Implementation | Introduction to Feature Engineering | Quiz: Introduction to feature engineering | Exercise on Feature Engineering | Overview of the module | Feature Transformation | Quiz: Feature Transformation | Feature Scaling | Quiz: Feature Scaling | Feature Encoding | Quiz: Feature Encoding | Combining Sparse classes | Quiz: Combining Sparse classes | Feature Generation: Binning | Quiz: Feature Generation- Binning | Feature Interaction | Quiz: Feature Interaction | Generating Features: Missing Values | Frequency Encoding | Quiz: Frequency Encoding | Feature Engineering: Date Time Features | Implementing DateTime Features | Quiz: Implementing DateTime Features | Automated Feature Engineering : Feature Tools | Implementing Feature tools | Quiz: Implementing Feature Tools | Exploring the NYC dataset | Predicting the NYC taxi trip duration | Predicting the NYC taxi trip duration
            Key Takeaways: Python libraries like Numpy, Pandas, etc. to analyze your data efficiently. | Importance of Statistics and Exploratory Data Analysis (EDA) in the data science field. | Linear Regression, Logistic Regression, and Decision Trees for building machine learning models. | Understand how to solve Classification and Regression problems using machine learning | How to evaluate your machine learning models using the right evaluation metrics? | Improve and enhance your machine learning model’s accuracy through feature engineering
            FAQs: Q: Who should take the Free Machine Learning Certification Course for Beginners? A: This course is meant for people looking to learn Machine Learning. We will start with understanding Python for Data Science, the importance of statistics and EDA, the underlying intuition behind several machine learning algorithms and then go on to solve case studies using Machine Learning concepts. | Q: Do I need to install any software before starting the course? A: You will get information about all installations as part of the course. | Q: Do I need to take the modules in a specific order? A: It is highly recommended to take the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get a machine learning certificate upon completion of the course? A: Yes, you will be given a certificate upon satisfactory completion of the Free Machine Learning Certification Course for Beginners.
            


key: chroma:document
string_value:
            Title: Data Science Career Conclave - Transition to Data Science!
            Description: It feels like half the world wants to move into data science these days, with spectacular perks and a plethora of openings on offer in the industry. Organizations are investing heavily in data science talent to stay or move ahead of their competitors. As a data science aspirant, you couldn’t have picked a better time to change your career!

But this comes with its own set of challenges. We are often asked by folks about how they should transition into data science. People from all sorts of backgrounds – IT, Sales, Finance, HR, Healthcare, etc. – they all want a piece of the data science pie.
In this exclusive course called the “Data Science Career Conclave”, Analytics Vidhya has brought together leading data science experts to share their view on a broad range of data science career topics.
 
What is being covered in this Data Science Career Conclave?
As we said, a broad range of topics related to transitioning into a data science career. Here’s a brief list of topics you can look forward to:

Different Roles in Data Science - Which Role is Right for You? - by Mathangi Sri
What are Hiring Managers Really Looking For? - by Kiran R
How to Build your Digital Profile for Data Science - by Dipanjan Sarkar
Panel Discussion: How can you Transition into Data Science in 12 Months?
            Curriculum: Introduction to the Career Conclave | Different Roles in Data Science - Which Role is Right For You? | What are Hiring Managers Really Looking For? | How to Build your Digital Profile for Data Science | Panel Discussion: How can you Transition into Data Science in 12 Months? | AI&ML Blackbelt Plus Program (Sponsored)
            Key Takeaways: Phone - 10 AM - 6 PM (IST) on Weekdays (Mon - Fri) on +91-8368808185 | Email [email protected] (revert in 1 working day) | Discussion Forum - answer in 1 working day
            FAQs: 
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/top-data-science-projects-for-analysts-and-data-scientists


key: chroma:document
string_value:
            Title: Top Data Science Projects for Analysts and Data Scientists
            Description: This is a very common question interviewers ask in data science interviews. We have conducted hundreds of these interviews for both data analyst and data scientist roles and this is quite often the jackpot question. This is especially true if you’re a fresher or a relative newcomer to data science.
Just doing courses or attaining certifications isn’t good enough. Almost everyone we know holds certifications in various aspects of data science. It adds no value to your resume if you don’t combine it with practical experience.
And that’s where open-source data science projects play such a key role!
            Curriculum: About the Data Science Projects Course | AI&ML Blackbelt Plus Program (Sponsored) | Machine Learning Visuals – A Brilliant Way to Communicate | PandaPy – Your New Favorite Python Library | VisualDL | Real-Time Audio Analysis using PyAudio | OpenAI’s Jukebox: A Generative Model for Music | Graph Neural Networks in TensorFlow 2.0 | Facebook AI's Detectron | Caire - Image Resizing | AlphaPose | FastPhotoStyle | Facebook AI’s DEtection TRansformer (DETR) | Real-Time Image Animation | Convert Any Image into a 3D Photo | Transform an Image into a Cartoon Illustration | One-Shot Multi-Object Tracking | GAN Compression | StyleGAN2 – A New State-of-the-Art GAN! | Real-Time Person Removal using TensorFlow.js | Computer Vision Basics in Microsoft Excel | Open AI's GPT-3 | NLP Paper Summaries | Google’s ELECTRA | Reformer – The Efficient Transformer in PyTorch | DeepReinforcementLearning | Minigo | The Goodreads Machine Learning Pipeline | Awesome Software Engineering for Machine Learning | TextShot | ShyNet – Privacy-Friendly and Cookie-Free Web Analytics | Coronavirus Time Series Data | Google Brain AutoML | ggbump – Data Visualization in R! | Google Earth Engine – 300+ Jupyter Notebooks to Analyze Geospatial Data | AVA – Automated Visual Analytics
            Key Takeaways: 
            FAQs: Q: I have decent programming experience but no background in data science or machine learning. Is this course right for me? A: It would be helpful if you have a working knowledge of at least the basic machine learning algorithms. While this course caters to both beginners and advanced users in data science, your existing knowledge will help you approach these projects with a clearer understanding of how to solve the problems.
We suggest taking the ‘Introduction to Data Science’ or ‘Applied Machine Learning’ courses first before tackling these projects if you don’t have any prior ML knowledge. | Q: What is the fee for the course? A: This course is free of cost. | Q: How much effort do I need to put in for this course? A: The idea behind this course is to expand and hone your existing data science skills. The more effort and time you put into these projects, the more you’ll learn. Take at least an hour or two every day and work on the projects that you are interested in. | Q: I’ve completed this course and have a good grasp on Top Data Science Projects for Analytics and Data Scientists. What should I learn next? A: Practice! Think of this as a never-ending journey - if you complete one project, take up another that caters to your domain or interests. The more your practice, the better you’ll become as a data science professional.
We also recommend writing about these projects. Once you finish one project, write down your approach, your learning, etc. and put that in a blog article. This will help you solidify what you learned and also help the community when they work on the same project. It’s a win-win for everyone! | Q: Can I download the projects in this course? A: Yes, all the projects are open source and we have provided the links to download them in the course itself. So help yourself and happy learning!
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/getting-started-with-git-and-github-for-data-science-professionals


key: chroma:document
string_value:
            Title: Getting Started with Git and GitHub for Data Science Professionals
            Description: Ever heard of version control? It is one of the most important concepts in a data scientist’s daily role - and yet most newcomers and beginners haven’t even come across it! This is a fallacy you must overcome as soon as possible.
You need to understand how to navigate through Git and GitHub if you want to make it as a data science professional. While a lot of folks know about these tools (having used them for cloning open source code from Google Research and other top data science organizations), they never really understand their real purpose.
The beauty of version control will be akin to a revelation. The way you can create a remote project and have all your team members work on different features parallelly yet independently but still have a stable running code at the end of the day - priceless! A lot of the problem we face in data science while working remotely and independently will be erased with a quick understanding of Git and GitHub.
Yes, this course really is that important!
            Curriculum: What is Git? | What is Github? | AI&ML Blackbelt Plus Program (Sponsored) | What is a Repository? | Understand Cloning | Let's Commit! | Understanding Push | Understanding Pull | Install Git in your system | Let's Initialize Git! | Configure Git in your system | Committing files in Git | View Logs in Git | Create Remote Repository | Add Git Remote to Your Repository | Push using Git | Cloning a GitHub Repository | Branching and Merging | Pull using Git | Forking and contributing to the world
            Key Takeaways: Learn the value and the in’s and out’s of Git and GitHub in this comprehensive guide for beginners | No prerequisites required - start from scratch and understand how Git and GitHub work | How you can use Git and GitHub to make your data science projects easier to track | You will add an essential data scientist skill to your portfolio after the course - version control!
            FAQs: Q: I have decent programming experience but no background in data science or machine learning. Is this course right for me? A: Absolutely! We have designed and taught this course in a way that caters to beginners coming from any background. Git and GitHub are two critical tools you should know as a data scientist or data science professional. The very idea of version control isn’t taught in many courses - and that’s a grave miss. You can learn all about it in this course and apply that when you start learning data science. | Q: What is the fee for the course? A: This course is free of cost. | Q: How much effort do I need to put in for this course? A: You’ll be able to finish this course in a week’s time if you spend an hour on it daily and explore it on your own along with what you learn here. | Q: I’ve completed this course and have a good grasp on how to navigate Git and GitHub. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. There are plenty of options to choose from. We suggest heading to courses.analyticsvidhya.com and browsing through the various offerings available.

You should also try and contribute to a few repositories based on your interests. That’s how you can build your data science portfolio! | Q: Can I download the videos in this course? A: We do not allow videos to be downloaded from the platform. You can visit this free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Machine Learning Starter Program
            Description: We generate more than 2.5 quintillion bytes of data every day - and companies across the globe are hiring data scientists to make sense of this data.
A data scientist’s job is one of the most sought-after in the 21st century. Companies are increasingly looking for professionals with a variety of skills in Machine Learning. And that's what we are here to give you via our Machine Learning Starter Program.
This is the perfect starting point to ignite your fledgling machine learning career and take a HUGE step towards your dream data scientist role.
            Curriculum: 
            Key Takeaways: Help you understand how this field is transforming and disrupting industries | Understand machine learning algorithms and apply them to solve real world problems | Enhance and complement your learning through real life projects | We believe in a holistic learning approach and that's how we've curated the Machine Learning Starter Program | Live interactive chat sessions on Monday to Friday between 7 PM to 8 PM IST. | Discussion Forum - answer in 1 working day
            FAQs: Q: Who should take this program? A: This program is meant for people looking to learn Machine Learning. | Q: Do I need to install any software before the course? A: You will get information about all installations as part of the program. | Q: Do I get a machine learning Starter Program certification upon completing the program? A: Yes, you will get the certificate upon completion of the program. | Q: How long can I access the program? A: You can access this program for 2 months from the date of your enrollment.
            


key: chroma:document
string_value:
            Title: Data Science Hacks, Tips and Tricks
            Description: The Data Science Hacks, Tips and Tricks course is your one stop destination to become a better and more efficient data scientist!
We have poured in our decades of experience with data science and programming (especially Python programming!), to provide you with time-saving hacks related to:

Python tips and tricks
Data exploration tips and tricks
Data preprocessing hacks
Efficient use of Jupyter notebooks
Python functions
Building predictive models (hacks to build machine learning models in no time!),


And much more! 
We have created the Data Science hacks, tips and tricks course in a way that you can go through each hack as a separate module. Since the goal of the hacks, tips and tricks is to provide you with efficient code to solve problems, the videos are a demo of these hacks, tips and tricks. The videos are self-explanatory.
This free course by Analytics Vidhya covers a broad range of data science hacks, tips and tricks, including Python programming hacks, tips and tricks to ace data science tasks like data preprocessing and data exploration, and much more. Get started today!
            Curriculum: About the Data Science Hacks, Tips and Tricks Course | AI&ML Blackbelt Plus Program (Sponsored) | Resource Downloader | Pandas Apply | Extract E-mails from text | Pandas Boolean Indexing | Pandas Pivot Table | str.split() | Normal Distribution | Remove Emojis from text | Elbow method for classifier | Pandas crosstab | MinMax Scaler | Feature engineering for time series data | Dummy data for Linear Regression | Image Augmentation | Tokenize by Hugging Face | Stratify - Splitting data proportionately | Reading HTML file | Divide Continuous and categorical data | Pandas Profilling | Formatting of DataFrames | Magic function- %history | Setting up Dark Jupyter notebook theme | Use Jupyter-themes to change cell width | Use parse_dates in read_csv | Use Jupyter nbviewer to share ipynb | Decision Tree Plotting | Reversing Dictionary | Interactive Plot using cufflinks | Using %%writefile and %run  magic functions | Feature Selection using Sklearn's SelectFromModel | Easiest way to convert string into characters | Pandarellel - Pandas in parallel | Date Parser | Resize Images | Regex 101
            Key Takeaways: 
            FAQs: Q: Who should take the Data Science Hacks, Tips and Tricks course? A: This course is for people who want to write more efficient data science code, learn programming hacks, or anyone who is curious about the different tips and tricks you can employ in a data science project! | Q: What is the fee for this course? A: This course is free of cost! | Q: How long would I have access to the “Data Science Hacks, Tips and Tricks” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete the "Data Science Hacks, Tips and Tricks" course in a few hours. As we mentioned above, each module covers a different hack, tip or trick so you can choose as per your convenience. | Q: Where can I apply these data science hacks? A:  | Q: I’ve completed this course already. What should I learn next? A:  | Q: Can I download videos from this course? A: We regularly update the "Data Science hacks, tips and tricks" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used in this course? A: We are primarily using Python to showcase these data science hacks. You might see a bit of R sprinked in from time to time!
            


key: chroma:document
string_value:
            Title: Introduction to Business Analytics
            Description: Getting Started with Business Analytics
What is Business Analytics? Why has it become so popular recently? What are some of the popular applications of Business Analytics? And more importantly, how can you get started with learning Business Analytics from scratch?
With growth in digitisation, Business Analytics is ubiquitous right now. Organizations are splurging to integrate data science solutions in their daily processes. This is where they need Business Analysts.
Why pursue Business Analytics:

Data is ubiquitous! Organizations need people who can use Business Analytics tools and techniques to make sense of this data. 
It is one of the hottest field in the industry right now
There are so many Business Analytics tools and techniques which can be applied to solve business problems. Keep learning, keep growing!
The potential of Business Analytics is limitless - spanning across industries, roles and functions
            Curriculum: What is Business Analytics? | Quiz: What is Business Analytics | You just joined an exicting startup! | Quiz - Map the Job families | Data Scientist vs. Data Engineer vs. Business Analyst | Quiz - Map the responsibilities | Sample problems and projects - Business Analytics vs. Data Science | Quiz: Sample problems and Projects - Business Analysts vs. Data Scientits | A few more things - Business Analytics vs. Data Science | Career in Business Analytics | Knowing Each other | AI&ML Blackbelt Plus Program (Sponsored) | Terms related to Business Analytics | Management Information Systems (MIS) | Detective Analysis | Business Intelligence | Predictive Modeling | Artificial Intelligence and Machine Learning | What kind of problems do Business Analysts work on? | Skills Required in Business Analytics Roles | Download the Roadmap for Certified Business Analytics Program (CBAP) | Bonus Section - Logistics of Certified Business Analytics Program from Analytics Vidhya | Overview - Case study - Ezine Publishing | Understanding Business | Quiz: Identify Focus Categories | Quiz: Getting Granular with traffic data | Quiz - Identify effective channel | Quiz - Maximize Revenue | Quiz - Target Customers | Quiz: Traffic Distribution | Quiz - Advertisements | Where to go from here?
            Key Takeaways: 
            FAQs: Q: What is the fee for this course? A: This course is free of cost. | Q: Can I download the videos from this course? A: You can visit this free course anytime to refer to these videos. | Q: Do I need to take the modules in a specific order? A: We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get a certificate upon completion of the course? A: No, there is no certificate for this course.
            


key: categories
string_value:
All Courses, 
        
      
        
          Tools, 
        
      
        
          Deep Learning Courses


key: chroma:document
string_value:
            Title: Introduction to PyTorch for Deep Learning
            Description: Welcome to the world of PyTorch - a deep learning framework that has changed and re-imagined the way we build deep learning models.
 
PyTorch was recently voted as the favorite deep learning framework among researchers. It has left TensorFlow behind and continues to be the deep learning framework of choice for many experts and practitioners.
 
PyTorch is super flexible and is quite easy to grasp, even for deep learning beginners. If you work on deep learning and computer vision projects, you’ll love working with PyTorch.
            Curriculum: Getting Started with PyTorch | Why should we use PyTorch? | A word from the creators of PyTorch | Tensors in PyTorch | Mathematical Operations in PyTorch(vs. NumPy) | Matrix Operations in PyTorch(vs. NumPy) | Tensor Operations | AI&ML Blackbelt Plus Program (Sponsored) | Getting started with Neural Networks | Exercise : Getting started with Neural Networks | Independent and Dependent Variables | Understanding Forward Propagation | Exercise : Forward Propagation | Error and Reason for Error | Exercise : Error and Reason for Error | Gradient Descent Intuition | Understanding Math Behind Gradient Descent | Exercise : Gradient Descent | Back Propagation | Exercise : Back Propagation | Summary of the Module | Modules in PyTorch - Autograd | Modules in PyTorch: Optim | Modules in PyTorch: nn | Implementing a Neural Network from Scratch | Case Study – Solving an Image Recognition problem in PyTorch | Other Use cases for  Deep Learning in PyTorch | What Next?
            Key Takeaways: 
            FAQs: Q: Who should take the Introduction to PyTorch for Deep Learning course? A: This course is designed for anyone who wants to learn PyTorch. So if you’re a newcomer to deep learning and aren’t sure which framework to pick up, this course is for you!

We have designed the course in a way that all newcomers will be able to start learning PyTorch. A basic knowledge of Python would be really helpful. | Q: I have decent programming experience but no background in machine learning or deep learning. I have never designed a neural network. Is this course right for me? A: Absolutely! We have designed the course in a way that will cater to newcomers and beginners. We start from scratch to get you onboard the PyTorch bandwagon and take things forward from there. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Introduction to PyTorch for Deep Learning” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this PyTorch course? A: You can complete the “Introduction to PyTorch for Deep Learning” course in a few hours. You are also expected to apply your knowledge of PyTorch and learning of this course to solve deep learning problems. The time taken in projects varies from person to person. | Q: I’ve completed this course and have decent knowledge about PyTorch. What should I learn next? A: That’s great! The next step in your journey should be all about learning deep learning concepts. Take the Computer Vision using Deep Learning course and start applying PyTorch on the concepts taught there. | Q: Can I download the videos in this course? A: We regularly update the “Introduction to PyTorch for Deep Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos. | Q: Which programming language is used to teach the Introduction to PyTorch for Deep Learning course? A: This course uses Python programming language throughout. If you need a refresher or are new to Python, you’ll find the free Python for Data Science course really useful.
            


key: chroma:document
string_value:
            Title: Introduction to Natural Language Processing
            Description: Natural Language Processing is the art of extracting information from unstructured text. Learn basics of Natural Language Processing, Regular Expressions & text sentiment analysis using machine learning in this course.
            Curriculum: Welcome to the Course | About the Course | Introduction to Natural Language Processing | Exercise : Introduction to Natural Language Processing | Python for Data Science (Optional) | AI&ML Blackbelt Plus Program (Sponsored) | Welcome to Module | Understanding Regular Expression | Implementing Regular Expression in Python | Exercise : Implementing Regular Expression in Python | Welcome to Module | Tokenization and Text Normalization | Exercise : Tokenization and Text Normalization | Exploring Text Data | Part of Speech Tagging and Grammar Parsing | Exercise : Part of Speech Tagging and Grammar Parsing | Implementing Text Pre-processing Using NLTK | Exercise : Implementing Text Pre-processing Using NLTK | Build a Basic ML Model for Text Classification | NLP Exam | Where to go from here?
            Key Takeaways: 
            FAQs: Q: Is it a good time to pursue Natural Language Processing? A: A good time to start with NLP is now! With plethora of applications in several markets & industries, NLP has become a highly sought after skill all over the world. | Q: What is the best language to learn for solving NLP tasks? A: Natural Language Processing with Python is the way to go and it has been the most popular language in both industry and Academia. Python provides excellent ready made libraries such as NLTK, Spacy, CoreNLP, Gensim, Scikit-Learn & TextBlob which have excellent easy to use functions to work with text data. Deep Learning frameworks like PyTorch, Tensorflow and Keras which are all part of Python Ecosystem are the default choices for using Deep Learning in Natural Langauge Processing. | Q: Do I need to have a PhD to build a career in NLP? A: Applied NLP is something that can be mastered by someone with good knowledge of Python and a background in Engineering or quantitative field is a good to have but not a necessity. Natural Language Processing with Python is something that can be taught and learnt with dedicated effort and a good learning path. | Q: Who should take this course? A: This course is for people who want to start their journey in working with text data and take their first step for understanding Natural Language Processing in an applied manner | Q: I have an experience of 2+ years and have no prior knowledge on NLP. Is the course right for me? A: The course assumes familiarity with Python and requires understanding of the basics of Machine Learning. No prior knowledge in Natural Language Processing is required. | Q: If I do not meet the requirements to enroll, what should I do? A: You can opt for our Free Course Python for Data science in order to get familiarised with Python | Q: What is the fee for this course? Will I get a certificate? A: This course is free of cost.
You will receive a certificate if you have enrolled in the course after 29th September,2020. | Q: How long would I have access to “Introduction to Natural Language Processing” course? A: Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete "Introduction to Natural Language Processing" course in a few hours. You are expected to work on the in course project and you can also participate and compete against other participants at Datahack.The time taken in projects varies from person to person. | Q: How can I apply and test my learnings about Natural Language Processing? A: You can start by doing the tests at the end of each lesson. In addition, you can apply Natural Language Processing to solve the following Practice Problems at Datahack: | Q: Can I download videos from this course? A: We regularly update "Introduction to Natural Language Processing" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used to teach Natural Language Processing in this course? A: This course uses Python programming language and its open source libraries NLTK and scikit-learn to solve the project
            


key: chroma:document
string_value:
            Title: Getting started with Decision Trees
            Description: What is a Decision Tree?
A Decision Tree is a flowchart like structure, where each node represents a decision, each branch represents an outcome of the decision, and each terminal node provides a prediction / label.

Why learn about Decision Trees?

Decision Trees are the most widely and commonly used machine learning algorithms.
Decision Trees can be used for solving both classification as well as regression problems.
Decision Trees are robust to Outliers, so if you have Outliers in your data - you can still build Decision Tree models without worrying about impact of Outliers on your model.
Decision Trees are easy to interpret and hence have multiple applications in different industries.
            Curriculum: Introduction to Decision Tree | Purity in Decision Trees | Quiz: Purity in Decision Trees | Terminologies Related to Decision Trees | Quiz: Introduction to Decision Trees | Terminologies Related to Decision Trees | How to Select the Best Split Point in Decision Trees | Quiz: How to Select the Best Split Point in Decision Trees | Chi-Square | Quiz: Chi-Square | Information Gain | Quiz: Information Gain | Reduction in Variance | Quiz: Reduction in Variance | Optimizing Performance of Decision Trees | Quiz: Optimizing Performance of Decision Trees | Decision Tree Implementation | Dataset: Decision Tree Implementation | Where to go from here? | AI&ML Blackbelt Plus Program (Sponsored)
            Key Takeaways: Introduction to Decision Trees - The course starts with basics of Decision Trees, the philosophy behind decision tree algorithm and why they are so popular among data scientists | Terminologies related to decision trees - Don't worry if you don't know anything about Decision Trees - that is the whole point about this course. What is a Leaf Node? Parent Node? Pruning? We will teach you all of these terminologies related to decision tree in a practical manner. | Different splitting criterion for decision tree like Gini, chi-square  The course teaches you different splitting criteria like Gini, Information Gain, chi-square and how do they impact the decision tree model | Implementation of decision tree in Python - The course will tell you several best practices you should keep in mind while implementing Decision Tree algorithm
            FAQs: Q: Who should take Getting Started with Decision Trees course? A: This course is for people who wants to learn the most commonly used tree based algorithm: Decision Tree algorithm along with its implementation in Python. | Q: I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me? A: The course assumes prior background in Machine Learning. So we would recommend you to be aware of basics of Machine Learning before going through this course. | Q: What is the fee for this course? A: This is a free course in Data Science. You only need to enrol in this free data science course and will be able to access the Decision Tree resources. | Q: How long would I have access to "Getting started with Decision Trees" course? A: Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete "Getting Started with Decision Trees" course in a few hours. You are also expected to apply Decision Trees and learning of this course to solve machine learning problems. The time taken in projects varies from person to person. | Q: How can I apply and test my learnings about Decision Trees? A: You can start by doing the tests at the end of various chapters. In addition, you can apply Decision Trees to solve various Practice problems on Analytics Vidhya DataHack Platform | Q: Can I download videos from this course? A: We regularly update "Getting started with Decision Trees" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used to teach Decision Trees in this course? A: This course used Python pogramming language and its open source libraries scikit-learn to teach you Decision Trees | Q: Do I get a certificate upon completion of the course? A: Yes! You will be awarded a Certificate upon completion of the course. Certificates will be awarded to those who have registered after 1 st September 2020 | Q: I just completed Decision Tree course, what should I do next? A: Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course | Q: I don't have Python Installed in my machine, what can I do? A: You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries | Q: How is a Free Course different from a paid course on Analytics Vidhya? A: Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles. | Q: Can I add this project on my resume and use it in my interview? A: Decision Tree is one of the favourite areas for machine learning interviewers. You should go ahead and showcase your learning today.
            


key: categories
string_value:
All Courses, 
        
      
        
          Tools, 
        
      
        
          Business Analytics Courses


key: chroma:document
string_value:
            Title: Introduction to Python
            Description: Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?
You have come to just the right place!

Most industry experts recommend starting your Data Science journey with Python
Across biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
Stackoverflow survey for 2019 had Python outrank Java in the list of most loved languages

Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.
Most data scientists have a few go-to libraries for their daily tasks like:

for performing data cleaning and analysis - pandas
for basic statistical tools – numpy, scipy
for data visualization – matplotlib, seaborn
            Curriculum: Overview of the Course | AI&ML Blackbelt Plus Program (Sponsored) | A brief introduction to Python | Introduction to Python Test | Installing Python | Become a BlackBelt in Data Science | Theory of Operators | Exercise | Understanding Operators in Python | Operators Test | Understanding variables and data types | Variable Test | Variables and Data Types in Python | Exercise | Understanding Conditional Statements | Exercise | Implementing Conditional Statements in Python | Conditional Statements test | Understanding Looping Constructs | Exercise | Implementing Looping Constructs in Python | Looping Constructs test | Understanding Functions | Implementing Functions in Python | Functions test | A brief introduction to data structure | Data Structure test | Understanding the concept of Lists | Lists test | Implementing Lists in Python | Exercise | Understanding the concept of Dictionaries | Exercise | Implementing Dictionaries in Python | Dictionaries test | Understanding the concept of Standard Libraries | Libraries test | Reading a CSV File in Python - Introduction to Pandas | Reading a CSV file in Python: Implementation | Reading a csv file in Python test | Understanding dataframes and basic operations | DataFrames and basic operations test | Reading dataframes and conduct basic operations in Python | Reading dataframes and conduct basic operations in Python Test | Indexing a Dataframe | Indexing DataFrames test | Exercise | Sorting Dataframes | Merging Dataframes | Quiz: Sorting and Merging dataframes | Apply function | Aggregating data | Quiz: Apply function and Aggregating data | Basics of Matplotlib | Data Visualization using Matplotlib | Quiz: Matplotlib | Basics of Seaborn | Data Visualization using Seaborn | Quiz: Seaborn | Understanding Regular Expressions | Quiz: Regular Expressions | Regular Expressions in  Python | Quiz: Regular Expressions in Python | Cheatsheet for Python | Instructions | Quiz | Python Coding Challenge | Poll | Where to go from here?
            Key Takeaways: 
            FAQs: Q: Who should take this course? A: This course is for people who want to learn basic Python skills and get started with Data Science using Python | Q: Do I get a certificate upon completion of the course? A: Yes! You will be awarded a Certificate upon completion of the course. Certificates will be awarded to those who have registered after 1 st September 2020 | Q: I have an experience of 2+ years, but no background in Data Science nor in Programming. Is the course right for me? A: The course assumes no prior background in Data Science or programming. We cover the basics of programming and Data Science in this course comprehensively. | Q: What is the fee for this course? A: This is a free course in Data Science. This course offers free python course | Q: How long would I have access to the “Python for Data Science” course? A: Once you register, you will have 6-month access to complete the course. If you visit the course 6 months after your initial registration - you will need to enrol in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete "Python for Data Science" course in a few hours. You are also expected to apply Python and learning of this course to perform basic data operations on data. The time taken in projects varies from person to person. | Q: How can I apply and test my learnings about Python? A: You can start by doing the tests at the end of various chapters. In addition, you can enroll for the Applied Machine Learning course to continue your Data Science and Machine Learning Journey. | Q: Can I download the videos from this course? A: We regularly update "Python for Data Science" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming languages is used to teach this course? A: This entire course uses Python(version 3) programming language and its open source libraries pandas, numpy, matplotlib and seaborn to teach you Data Science.
            


key: categories
string_value:
All Courses, 
        
      
        
          Projects, 
        
      
        
          Machine Learning Courses


key: chroma:document
string_value:
            Title: Loan Prediction Practice Problem (Using Python)
            Description: This course is designed for people who want to solve binary classification problems. Classification is a skill every Data Scientist should be well versed in.
In this course, we are solving a real life case study of Dream Housing Finance. The company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.
By the end of the course, you will have a solid understanding of Classification problem and Various approaches to solve the probem
            Curriculum: Introduction to the Course | Table of Contents | Problem Statement | Hypothesis Generation | Exercise 2  | Discussion | Getting the system ready and loading the data | Understanding the Data | Univariate Analysis | Bivariate Analysis | Missing Value and Outlier Treatment | Evaluation Metrics for Classification Problems | Model Building : Part I | Logistic Regression using stratified k-folds cross validation | Feature Engineering | Model Building : Part II | AI&ML Blackbelt Plus Program (Sponsored)
            Key Takeaways: 
            FAQs: Q: Who should take this course? A: This course is meant for people looking to explore Classification Problems in Python. | Q: Do I need to install any software before starting the course? A: You should have Python and Jupyter Notebook installed on your system. | Q: What is the refund policy? A: The course is free of charge. | Q: Do I need to take the modules in a specific order? A: We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get certificate upon completion of the course? A: This is a free course and therefore there is no certificate involved. | Q: What is the fee for this course? A: The course is free of charge. | Q: Is there any placement support? A: This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal.
            


key: chroma:document
string_value:
            Title: Big Mart Sales Prediction Using R
            Description: Sales prediction is a very common real life problem that each company faces at least once in its life time. If done correctly, it can have a significant impact on the success and performance of that company.
In this course you will be working on the Big Mart Sales Prediction Challenge.
The course will equip you with the skills and techniques required to solve regression problems in R. You will be provided with sufficient theory and practice material to hone your predictive modeling skills.
            Curriculum: Overview of the Course | Table of contents | Problem Statement | Hypothesis Generation | Loading Packages and Data | Understanding the Data | Univariate Analysis | Bivariate Analysis | Missing Value Treatment | Feature Engineering | Encoding Categorical Variables | PreProcessing Data | Model Building | Linear Regression | Regularized Linear Regression | Random Forest | XGBoost | Summary | AI&ML Blackbelt Plus Program (Sponsored)
            Key Takeaways: 
            FAQs: Q: Who should take this course? A: This course is meant for people looking to learn solving regression problems using R. | Q: Do I need to install any software before starting the course? A: You will need to download and install R and RStudio | Q: What is the refund policy? A: The course is free of charge. | Q: Do I need to take the modules in a specific order? A: We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get certificate upon completion of the course? A: This is a free course and therefore there is no certificate involved. | Q: What is the fee for this course? A: The course is free of charge. | Q: How long I can access the course? A: You will have access to the course for a duration of 6 months. | Q: Is there any placement support? A: This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal
            


key: chroma:document
string_value:
            Title: Twitter Sentiment Analysis
            Description: What is Sentiment Analysis?

Sentiment Analysis or Opinion Mining is a technique used to analyse the emotion in a text. We can extract the attitude or the opinion of a piece of text and get insights on it. 
In the context of machine learning, you can think of Sentiment Analysis as a Classification problem where the text can either have a positive sentiment, a negative sentiment or a neutral one.


What are the applications of Sentiment Analysis in the industry?

In the age of social media, it is extremely common to comment about 
a movie you liked or 
a book you didn’t like or 
a product you bought was not up to the mark.


Therefore, a lot of companies use sentiment analysis for their products since it provides direct feedback of the customer’s opinion.
It is also important to detect and remove hateful content from social media and companies like Twitter, Facebook, etc. extensively use sentiment analysis on a daily basis.

 
On what kind of projects would I implement sentiment analysis?
There are a wide variety of projects where you can use Sentiment Analysis. Here are a couple of popular use cases:

Sentiment Analysis can not only be used for customer reviews or product feedback, but in other domains as well.
Analyzing the sentiments on social media on the US Elections, for example, gives useful insights on which candidates are favoured by the public and which are not.

For other interesting problems involving sentiment/emotion detection, you can visit: https://datahack.analyticsvidhya.com/contest/all/

What is the range of sentiments that can be observed and analysed?

In the earlier days of Natural language processing and Sentiment Analysis, the sentiments could hold only 2 or 3 values: Positive or Negative, and Positive, Neutral or Negative.
However, with the advent of deep learning, we can now recognize the subtle emotions in a text as well.
This has made tasks like Sarcasm detection, fake news detection etc. popular in research areas of Natural language processing 


Can I add this project to my resume and use it in my Interview?

Sentiment Analysis is one of the most popular applications of Machine Learning and Classification in Natural language processing
We also encourage you to take up more diverse datasets and apply sentiment analysis on them.
Sentiment Analysis is also one of the first projects you would learn in your Natural language processing journey and as such is commonly asked in interviews.
            Curriculum: Overview of the Course | Understand the Problem Statement | Table of Contents | Loading Libraries and Data | Data Inspection | Data Cleaning | Story Generation and Visualization from Tweets | Bag-of-Words Features | TF-IDF Features | Word2Vec Features | Modeling | Logistic Regression | Support Vector Machine (SVM) | RandomForest | XGBoost | FineTuning XGBoost + Word2Vec | Summary | AI&ML Blackbelt Plus Program (Sponsored)
            Key Takeaways: 
            FAQs: Q: Who should take this course? A: This course is for people who want to learn Sentiment Analysis and Text Classification using Python. | Q: Do I need to install any software before starting the course? A: You should have Python and Jupyter Notebook installed on your system. | Q: How long would I have access to “Twitter Sentiment Analysis” course? A: Once you register, you will have a 6-month access to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: Do I need to take the modules in a specific order? A: We would highly recommend taking the course in the order in which it has been designed to gain the maximum knowledge from it. | Q: Do I get certificate upon completion of the course? A: This is a free course and therefore there is no certificate involved. | Q: What is the fee for this course? A: The course is free of charge. | Q: Is there any placement support? A: This is an introductory course and this does not include any placement support. Once you have worked on a few data science projects and hackathons, you can always apply to jobs on Analytics Vidhya portal. | Q: How can I apply and test my learnings about Sentiment Analysis? A: You can start by doing the tests at the end of various chapters. In addition, you can enrol for the Natural Language Processing (NLP) Using Python to continue your Natural language processing Journey. | Q: How much effort will this course take? A: You can complete "Twitter Sentiment Analysis" course in a few days. You are also expected to apply the concepts explained and apply them on the data yourself. The time taken in projects varies from person to person.
            


key: categories
string_value:
All Courses, 
        
      
        
          Tools, 
        
      
        
          Machine Learning Courses


key: chroma:document
string_value:
            Title: Pandas for Data Analysis in Python
            Description: Pandas is one of the most popular Python libraries in data science. In fact, Pandas is among those elite libraries that draw instant recognition from programmers of all backgrounds, from developers to data scientists.
According to a recent survey by StackOverflow, Pandas is the 4th most used library/framework in the world. That is quite an achievement!
Pandas is the first library we import when we fire up our Jupyter notebooks (‘import pandas as pd’ is indelibly etched in our minds!). It is a super flexible tool that enables us to perform data analysis and data manipulation on Pandas dataframes in double-quick time.
            Curriculum: Introduction to the Course | Pandas Installation | AI&ML Blackbelt Plus Program (Sponsored) | Loan Prediction | Big Mart Sales | Understanding File System & shell commands | Reading Excel & CSV files | Writing Data using Pandas | Quiz: Reading a csv file using Pandas | What are Pandas Dataframes & its operations? | Selecting Columns & Rows in Pandas (Indexing) | Quiz: DataFrames and basic operations | Basic Descriptive Statistics using Pandas | Plotting using Pandas | Quiz: Data Exploration using Pandas | Renaming Column using Pandas | Sorting Data in Pandas DataFrame | Binning using Pandas | Handling Missing Values | Apply Function in Pandas for Element wise Operations | Quiz: Pandas Apply Function | Types of Aggregations in Pandas | Aggregations using Pandas in action | Quiz: Aggregations in Pandas | Merging Data in Pandas Dataframes | Quiz: Merging Data using Pandas | Pandas Cheatsheet
            Key Takeaways: Beginners in Python who are curious about Pandas and how to use it for data analysis and data manipulation | Anyone who wants to start their data science career (Pandas is the first library you’ll import!) | Anyone looking to get into a data analyst role using Python programming | Anyone who wants to jump from Excel into Python
            FAQs: Q: What is Pandas? A: Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation library built in Python.
Pandas is THE most popular Python library in data science and the 4th most popular library in the world (according to StackOverflow’s global survey). The open source nature of Pandas isone of the primary reasons for its popularity and adoption rate in the community.
Here’s a golden nugget about Pandas from Wikipedia:
The name ‘Pandas’ is derived from the term "panel data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. | Q: How can I install Pandas in Python? A: The easiest way to install Pandas is to install it as part of the Anaconda distribution. Anaconda is free and easy to install (it’s the installer we use for setting up Python as well).
We highly recommend doing this instead of trying to install Pandas from scratch (it will be a slightly difficult process if you’re not familiar with Python or programming in general). | Q: Where is Pandas used in data science? A: Pandas is primarily used in data science and machine learning in the form of dataframes. As we’ve mentioned above, Pandas enables us to perform all sorts of data analysis and manipulation tasks in Python, including importing different data files like CSV, Excel, JSON, etc.
Most data science projects use Pandas to perform aggregating functions like GroupBy, merge and join dataframes, impute missing values in Python, among other things.
In short, Pandas is an essential part of a data science project! | Q: What is a Pandas dataframe? A: In Pandas, a dataframe is a data structure used to store and manipulate tabular data. The tabular data has it’s columns, column names and rows - we can easily perform operations on large dataframes using Pandas functions.

A Pandas dataframe is also the standard structure used to store the data from common formats of data like CSV file, Excel sheets and others. | Q: How difficult is it to learn Pandas? A: It’s actually quite straightforward! Even though Pandas has a ton of features and functions, you can easily pick those up with a bit of practice.
And that’s exactly how we’ve designed the course! You’ll learn all the different Pandas functions in Python and then work on various exercises after each lesson to solidify what you’ve learned. | Q: Do I need to know Python to learn Pandas? A: It would definitely help to have basic Python programming knowledge if you want to maximize your Pandas experience. The ability to merge or join Pandas dataframes, manipulating data and so on will require a bit of Python programming.
If you’re completely new to Python, we recommend taking our free Python for Data Science course. | Q: What kind of data analysis can I perform using Pandas? A: You can perform all kinds of data analysis and data manipulation using Pandas. We cover the key points in this course but here is a list for your reference:
●	Reading and writing data from different file formats like CSV, Excel, JSON, etc.
●	Data alignment
●	Handling missing data
●	Reshaping data and building pivot tables using Pandas
●	Label-based slicing, fancy indexing, and subsetting of large datasets
●	Column insertion and deletion using Pandas dataframes
●	Group by allowing split-apply-combine operations on datasets
●	Dataset merging and joining
●	Filtering dataframes, and so on | Q: Will Pandas help me become a better data scientist? A: Short answer - yes. Which data science project won’t appreciate someone who can perform quick analysis and data manipulation? This is a key part of any data science project and mastering Pandas will go a long way towards making you a better and more efficient data scientist.
Additionally, this will also help you in your interview rounds when you’re asked to analyze certain data (Pandas is your best friend!). | Q: What kind of data formats can I import using Pandas? A: The beauty about Pandas is the remarkable number of files we can read into Python. Here’s a quick list:
1. Comma-separated values (CSV)
2. XLSX
3. ZIP
4. Plain Text (txt)
5. JSON
6. XML
7. HTML
8. Images
9. Hierarchical Data Format
10. PDF
11.DOCX
12. MP3
13. MP4
14. SQL | Q: Who should take Pandas for Data Analysis in Python course? A: This course is for people who wants to start their journey in machine learning and data analysis. Pandas is one of the most popular data analysis libraries in Python. | Q: I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me? A: The course assumes no prior background in Machine Learning. So, you can start this course. | Q: What is the fee for this course? A: This course is free of cost. | Q: How long would I have access to "Pandas for Data Analysis in Python" course? A: Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete Pandas for Data Analysis in a few hours. You are also expected to apply Pandas and its functions to perform data analysis. | Q: How can I apply and test my learnings about Pandas for Data Analysis? A: You can start by doing the tests at the end of various chapters. In addition, you can apply Pandas for Data Analysis to solve various Practice problems on Analytics Vidhya DataHack Platform | Q: Can I download videos from this course? A: We regularly update "Pandas for Data Analysis in Python" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used to teach Pandas in this course? A: As the name suggests, this course teaches Pandas for Data Analysis in Python. | Q: Do I get a certificate upon completion of the course? A: No, there is no certificate for this course. | Q: I just completed Pandas for Data Analysis in Python course, what should I do next? A: Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course | Q: I don't have Python Installed in my machine, what can I do? A: You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries | Q: How is a Free Course different from a paid course on Analytics Vidhya? A: Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles.
            


key: chroma:document
string_value:
            Title: Support Vector Machine (SVM) in Python and R
            Description: Want to learn the popular machine learning algorithm - Support Vector Machines (SVM)? Support Vector Machines can be used to build both Regression and Classification Machine Learning models.
This free course will not only teach you basics of Support Vector Machines (SVM) and how it works, it will also tell you how to implement it in Python and R.
This course on SVM would help you understand hyperplanes and Kernel tricks to leave you with one of the most popular machine learning algorithms at your disposal.
            Curriculum: What are Support Vector Machines? | Why do we use SVM and how is it better? | AI&ML Blackbelt Plus Program (Sponsored) | Non-linear Separation and Margins | Hyperplanes in SVM | Quiz: Support Vector Machine | Types of Kernels used in SVM | Quiz: Kernel Tricks | Hyperparameter tuning in SVM | Implementing Support Vector Machine | How to implement Support Vector Machine Classifier in R? | Drawbacks of SVM | What next?
            Key Takeaways: Anyone who wants to start their machine learning career and is looking to learn the different machine learning algorithms | Anyone who wants to become a data scientist or a machine learning engineer | Anyone interested in learning how a classic machine learning algorithm like SVM works!
            FAQs: Q: What is a Support Vector Machine (SVM)? A: “Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression problems. SVM is one of the most popular algorithms in machine learning and we’ve often seen interview questions related to this being asked regularly. | Q: Is SVM a must-know algorithm in machine learning? A: SVM is definitely an algorithm every data scientist or machine learning engineer should know. It is a widely adopted algorithm across organizations.

Even on a personal learning front, learning SVM and how it works will add a lot to your own machine learning knowledge. | Q: Can I perform both classification and regression using SVM? A: Yes! As we mentioned above, you can use Support Vector Machine (SVM) for both classification and regression problems. However, SVM is popularly used far more for classification tasks.

This is something you will learn inside the course as well. You will get an intuitive understanding as to why SVM gives such an accurate output when we apply it on classification problems. | Q: What kind of machine learning problems can I solve using SVM? A: Support Vector Machine can be used for a wide variety of classification problem like, text classification, default loan prediction. You can try implementing SVM on different kinds of datasets to learn it in detail (https://datahack.analyticsvidhya.com/contest/all/) | Q: What do I need to know before learning SVM? A: There are three key components you would need to know before jumping to SVM:
A working knowledge of Python (we recommend the Python for Data Science course)
If not Python, then a working knowledge of R
Basics of Machine Learning (what is supervised learning, what is classification and regression, etc.). You can take the ‘Introduction to Data Science’ course to learn all of these concepts | Q: What is an SVM classifier? A: The SVM classifier is basically a machine learning algorithm which is used for classification tasks. We mainly use it for classifying data which can’t be separated by a straight line. | Q: Can I use Support Vector Machine algorithm in a Kaggle or DataHack hackathon? A: Of course! You should always try out as many algorithms as you can in machine learning competitions. We always encourage our community to experiment on DataHack and Kaggle projects and hackathons.

You can apply what you learn in this course on Kaggle and DataHack hackathons for sure. | Q: How difficult is it to learn SVM for a beginner in machine learning? A: SVM can be tricky….if you aren’t paying attention. If you follow the modules and lessons in this course, you’ll be a Support Vector Machine expert in a few hours!

Remember - practice is key. The more you practice your newly acquired SVM knowledge, the better you will become. Apply your learning on the various classification projects on the DataHack platform. | Q: What is a SVM kernel? A: Since SVM is primarily used to classify non-linearly separable data, it provides a variety of functions to segregate the classes. These functions are called kernels. You can study more about these functions in the course and observe how SVM works. | Q: Who should take Support Vector Machines in Python & R course? A: This course is for people who wants to learn one of the most popular machine learning algorithm - Support Vector Machines along with its implementation in Python and R. | Q: I have a programming experience of 2+ years, but I have no background of Machine learning. Is the course right for me? A: The course assumes prior background in Machine Learning. So we would recommend you to be aware of basics of Machine Learning before going through this course. | Q: What is the fee for this course? A: This course is free of cost. | Q: How long would I have access to "Support Vector Machines in Python and R" course? A: Once you register, you will have 6 month access to complete the course. If you visit the course 6 month after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete Support Vector Machines in Python and R in a few hours. You are also expected to apply SVM to build a machine learning classifier. | Q: How can I apply and test my learnings about Support Vector Machines? A: You can start by doing the tests at the end of various chapters. In addition, you can apply Support Vector Machines to solve various Practice problems on Analytics Vidhya DataHack Platform | Q: Can I download videos from this course? A: We regularly update "Support Vector Machines in Python and R" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used to teach Support Vector Machines in this course? A: This course teaches Support Vector Machines in both Python and R. | Q: Do I get a certificate upon completion of the course? A: No, there is no certificate for this course. | Q: I just completed Support Vector Machine course, what should I do next? A: Congratulations! We would highly recommend that you continue your machine learning journey by taking our Applied Machine Learning Course | Q: I don't have Python Installed in my machine, what can I do? A: You can go ahead and install Anaconda distribution - it will come pre-installed with everything you need including Pandas and scikit-learn libraries | Q: How is a Free Course different from a paid course on Analytics Vidhya? A: Our free courses are just the tip of the iceberg. They are good to get you started, where as paid course provide you with the depth required for industry roles. | Q: Can I add this project on my resume and use it in my interview? A: Support Vector Machine is a very important algorithm in Machine Learning. Interviewers often ask questions about Kernel tricks and SVM implementation. You should go ahead and showcase your learning today.
            


key: chroma:document
string_value:
            Title: Evaluation Metrics for Machine Learning Models
            Description: Evaluation metrics form the backbone of improving your machine learning model. Without these evaluation metrics, we would be lost in a sea of machine learning model scores - unable to understand which model is performing well.
 
Wondering where evaluation metrics fit in? Here’s how the typical machine learning model building process works:

We build a machine learning model (both regression and classification included)
Get feedback from the evaluation metric(s)
Make improvements to the model
Use the evaluation metric to gauge the model’s performance, and
Continue until you achieve a desirable accuracy

 
Evaluation metrics, essentially, explain the performance of a machine learning model. An important aspect of evaluation metrics is their capability to discriminate among model results.
 
If you’ve ever wondered how concepts like AUC-ROC, F1 Score, Gini Index, Root Mean Square Error (RMSE), and Confusion Matrix work, well - you’ve come to the right course!
            Curriculum: Types of Machine Learning | Why do we need Evaluation Metrics? | AI&ML Blackbelt Plus Program (Sponsored) | Confusion Matrix | Quiz: Confusion Matrix | Accuracy | Quiz: Accuracy | Alternatives of Accuracy | Quiz: Alternatives of Accuracy | Precision and Recall | Quiz: Precision and Recall | F-Score | Thresholding | AUC-ROC | Quiz: AUC-ROC | Log Loss | Quiz: Log Loss | Gini Coefficient | MAE and MSE | RMSE and RMSLE | Quiz: RMSE and RMSLE | R2 and Adjusted R2 | R2 and Adjusted R2 | Cross-Validation | The Way Forward
            Key Takeaways: 
            FAQs: Q: Who should take the Evaluation Metrics for Machine Learning Models course? A: This course is designed for anyone who wants to learn how to evaluate their machine learning models. So if you’re a newcomer to machine learning and want to improve your model’s performance, this course is for you! | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Evaluation Metrics for Machine Learning Models” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Evaluation Metrics for Machine Learning Models” course in a few hours. | Q: I’ve completed this course and have decent knowledge about evaluating machine learning models. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course to understand the end-to-end machine learning pipeline, and how evaluation metrics play a part there. | Q: Can I download the videos in this course? A: 
            


key: chroma:document
string_value:
            Title: Fundamentals of Regression Analysis
            Description: Linear regression and logistic regression are typically the first algorithms we learn in data science. These are two key concepts not just in machine learning, but in statistics as well.
 
Due to their popularity, a lot of data science aspirants even end up thinking that they are the only forms of regression! Or at least linear regression and logistic regression are the most important among all forms of regression analysis.
 
The truth, as always, lies somewhere in between. There are multiple types of regression apart from linear regression:

Ridge regression
Lasso regression
Polynomial regression
Stepwise regression, among others.

 
Linear regression is just one part of the regression analysis umbrella. Each regression form has its own importance and a specific condition where they are best suited to apply.
 
Regression analysis marks the first step in predictive modeling. The different types of regression techniques are widely popular because they’re easy to understand and implement using a programming language of your choice.
            Curriculum: Welcome! | What is Regression Analysis? | Why do we use Regression? | AI&ML Blackbelt Plus Program (Sponsored) | How many types of regression techniques do we have? | Introduction to Linear Models | Understanding Cost function | Understanding Gradient descent (Intuition) | Maths behind gradient descent | Convexity of cost function | Assumptions of Linear Regression | Implementing Linear Regression | Generalized Linear Models | Introduction to Logistic Regression | Odds Ratio | Implementing Logistic Regression | Multiclass using Logistic Regression | Challenges with Linear Regression | What is Ridge Regression? | Notebook | What is Lasso Regression? | Implementation | How to select the right regression model? | What Next?
            Key Takeaways: 
            FAQs: Q: Who should take the Fundamentals of Regression Analysis course? A: This course is aimed towards beginners in data science, machine learning and even statistics. Regression analysis and the different forms of regression like linear regression are key concepts in these fields.

We have designed the course such that even newcomers will be able to follow along easily and be able to build regression models by the end of the course! | Q: I have some programming experience but no background in machine learning or statistics. Is this course right for me? A: Absolutely! The Fundamentals of Regression Analysis course is easy to follow along and we have provided the appropriate resources where necessary throughout the course. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Fundamentals of Regression Analysis” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Fundamentals of Regression Analysis” course in a few hours. You are also expected to apply your knowledge of the different regression types and learning of this course to solve machine learning problems. The time taken in projects varies from person to person. | Q: I’ve completed this course and have decent knowledge about Regression Analysis, linear regression, and logistic regression. What should I learn next? A: That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course.

That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course! | Q: Can I download the videos in this course? A: We regularly update the “Fundamentals of Regression Analysis” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos. | Q: Which programming language is used to teach the Introduction to Fundamentals of Regression Analysis course? A: 
            


key: chroma:document
string_value:
            Title: Getting Started with scikit-learn (sklearn) for Machine Learning
            Description: Scikit-learn, or sklearn for short, is the first Python library we turn to when building machine learning models. Sklearn is unanimously the favorite Python library among data scientists. As a newcomer to machine learning, you should be comfortable with sklearn and how to build ML models, including:

Linear Regression using sklearn
Logistic Regression using sklearn, and so on.

 There’s no question - scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn (sklearn) ranks in the top echelon along with Pandas and NumPy.
We love the clean, uniform code and functions that scikit-learn provides. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models using sklearn.
In short, sklearn is a must-know Python library for machine learning. Whether you want to build linear regression or logistic regression models, decision tree or a random forest, sklearn is your go-to library.
            Curriculum: Welcome to this course | What is scikit-learn? | Components of scikit-learn | Community / Organizations using scikit-learn | Introduction to Data Science Life Cycle | Scikit-learn for Data Preprocessing | Treating missing values | Treating Outliers | Feature Engineering | Dimensionality Reduction | Introduction to Model Building and Evaluation | Regression | Classification | Clustering | Introduction | Understanding Problem Statement | Building a prototype model | Data Exploration and Preprocessing | Encode the categorical variables | Scale the data | Model Building | Feature Importance | Identifying features to build the ML pipeline | Pipeline Design | Building Pipeline | Predict the Target | Conclusion
            Key Takeaways: 
            FAQs: Q: Who should take the Getting Started with scikit-learn (sklearn) for Machine Learning course? A: This course is designed for anyone who wants to get started with machine learning. So whether you’re a machine learning aspirant who is just starting out or a team leader looking to understand how it all works, this course is for you. | Q: I have some programming experience but no background in machine learning. Is this course right for me? A: Sure. You will be able to follow along with the Python code but it’ll definitely help if you know basic machine learning algorithms, like linear regression, logistic regression, and decision trees. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Getting Started with scikit-learn (sklearn) for Machine Learning” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Getting Started with scikit-learn (sklearn) for Machine Learning” course in a few hours. You are also expected to apply your knowledge and learning of this course to solve machine learning problems. The time taken in projects varies from person to person. | Q: I’ve completed this course and have decent knowledge about sklearn and the different machine learning algorithms. What should I learn next? A: That’s great! We highly recommend expanding your skillset and portfolio by taking the next step in the Applied Machine Learning course.

That is a comprehensive course covering the entire end-to-end machine learning pipeline and includes a thorough deep dive into the various machine learning algorithms, including linear regression and logistic regression, of course! | Q: Can I download the videos in this course? A: We regularly update the “Getting Started with scikit-learn (sklearn) for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos. | Q: Which programming language is used to teach the Getting Started with scikit-learn (sklearn) for Machine Learning course? A: This course uses Python programming language throughout
            


key: chroma:document
string_value:
            Title: Convolutional Neural Networks (CNN) from Scratch
            Description: Convolutional Neural Networks, or CNN as they’re popularly called, are the go-to deep learning architecture for computer vision tasks, such as object detection, image segmentation, facial recognition, among others. CNNs have even been extended to the field of video analysis!
If you are picking one deep learning architecture to learn and are not sure where to start, you should go for convolutional neural networks. Deep learning enthusiasts and experts with CNN knowledge are being widely sourced in the industry.
It’s your time to use this CNN skillset and shine!
            Curriculum: What is a Neural Network? | Types of Neural Networks | Prerequisites | AI&ML Blackbelt Plus Program (Sponsored) | What is a Convolutional Neural Network? | Why should you use a CNN | The Convolutional Layer | The Pooling Layer | The Ouput Layer | Taking a step back: The bigger picture of CNNs | Transforming the data | Forward Propagation | Backpropagation | Using NumPy | Using Keras | Implementing a CNN in PyTorch | More projects with CNN
            Key Takeaways: 
            FAQs: Q: Who should take the Convolutional Neural Networks (CNN) from Scratch course? A: This course is designed for anyone who wants to learn all about convolutional neural networks, how CNNs work, the different components of CNN, and how to implement CNN from scratch in Python for deep learning. | Q: I have decent programming experience but no background in deep learning. Is this course right for me? A: You would need to know the basics of neural networks. We suggest taking the popular ‘Introduction to Neural Networks’ course on Analytics Vidhya. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Convolutional Neural Networks (CNN) from Scratch” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Convolutional Neural Networks (CNN) from Scratch” course in a few hours. | Q: I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Computer Vision using Deep Learning” course | Q: Can I download the videos in this course? A: We regularly update the “Convolutional Neural Networks (CNN) from Scratch” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Dimensionality Reduction for Machine Learning
            Description: Have you worked on a dataset with more than a thousand features? How about 40,000 features? We are generating data at an unprecedented pace right now and working with massive datasets in machine learning projects is becoming mainstream.
This is where the power of dimensionality reduction techniques comes to the fore. Dimensionality reduction is actually one of the most crucial aspects in machine learning projects.
You can use dimensionality reduction techniques to reduce the number of features in your dataset without having to lose much information and keep (or improve) the model’s performance. It’s a really powerful way to deal with huge datasets, as you’ll see in this course!
Every data scientist, aspiring established, should be aware of the different dimensionality reduction techniques, such as Principal Component Analysis (PCA), Factor Analysis, t-SNE, High Correlation Filter, Missing Value Ratio, among others.
So in this beginner-friendly course, you will learn the basics of dimensionality reduction and why you should know dimensionality reduction in machine learning. We will also cover 12 dimensionality reduction techniques! This course is as comprehensive an introduction as you can get!
            Curriculum: Introduction | AI&ML Blackbelt Plus Program (Sponsored) | What is Dimensionality Reduction? | Why is Dimensionality Reduction required? | Common Dimensionality Reduction Techniques | Missing Value Ratio | Missing Value Ratio Implementation | Low Variance Filter | Low Variance Filter Implementation | High Correlation Filter | Backward Feature Elimination | Backward Feature Elimination Implementation | Forward Feature Selection | Forward Feature Selection Implementation | Random Forest | Introduction to the Module | Factor Analysis | Principal Component Analysis | Independent Component Analysis | Understanding Projection | ISOMAP | t- Distributed Stochastic Neighbor Embedding (t-SNE) | UMAP
            Key Takeaways: 
            FAQs: Q: Who should take the Dimensionality Reduction for Machine Learning course? A: This course is designed for anyone who wants to learn about the different dimensionality reduction techniques, such as PCA and Factor Analysis. So if you’re a newcomer to machine learning and want to understand how to work with a dataset containing multiple features, this course is for you! | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Dimensionality Reduction for Machine Learning” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Dimensionality Reduction for Machine Learning” course in a few hours. | Q: I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next? A:  | Q: Can I download the videos in this course? A: We regularly update the “Dimensionality Reduction for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: K-Nearest Neighbors (KNN) Algorithm in Python and R
            Description: K-Nearest Neighbor (KNN) is one of the most popular machine learning algorithms. As a newcomer or beginner in machine learning, you’ll find KNN to be among the easiest algorithms to pick up.
And despite its simplicity, KNN has proven to be incredibly effective at certain tasks in machine learning.
 
The KNN algorithm is simple to understand, easy to explain and perfect to demonstrate to a non-technical audience (that’s why stakeholders love it!). That’s a key reason why it’s widely used in the industry and why you should know how the algorithm works.
            Curriculum: Welcome to the Course | What is KNN? | Applications of KNN | Steps to build a KNN model | Determining right value of k | How to Calculate distance? | Issues with distance based algorithms | Implementation of KNN in Pyhton | Implementation of KNN in R | More resources for you
            Key Takeaways: 
            FAQs: Q: Who should take the K-Nearest Neighbor (KNN) Algorithm in Python and R course? A: This course is designed for anyone who wants to understand what the KNN algorithm is and how it works in machine learning. This is an important concept in machine learning that’s widely used in the industry. All the more reason to start learning today! | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: You should ideally have a basic grasp on machine learning algorithms and know the difference between regression and classification. We suggest enrolling in the Introduction to Data Science course for starters. | Q: What is the fee for the course? A: This course is free of cost! All you need to do is sign up and get started. | Q: How long would I have access to the “K-Nearest Neighbor (KNN) Algorithm in Python and R” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “K-Nearest Neighbor (KNN) Algorithm in Python and R” course in a few hours. | Q: I’ve completed this course and have a good grasp on the KNN algorithm. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course. | Q: Can I download the videos in this course? A: 
            


key: chroma:document
string_value:
            Title: Ensemble Learning and Ensemble Learning Techniques
            Description: Ensemble learning is a powerful machine learning algorithm that is used across industries by data science experts. The beauty of ensemble learning techniques is that they combine the predictions of multiple machine learning models. You must have used or come across several of these ensemble learning techniques in your machine learning journey:- Bagging- Boosting- Stacking- Blending, etc. These ensemble learning techniques include popular machine learning algorithms such as XGBoost, Gradient Boosting, among others. You must be getting a good idea of how vast and useful ensemble learning can be!
            Curriculum: Intuition behind Ensemble Learning | What is Ensemble Learning? | What models will be covered in the course? | Quiz: Introduction to Ensemble Learning | AI&ML Blackbelt Plus Program (Sponsored) | Max Voting | Averaging | Weighted Average | Quiz: Basic Ensemble Techniques | Stacking | Implementing Stacking | Variants of Stacking | Blending | Bootstrap Sampling | Quiz: Bootstrap Sampling | What is Bagging? | Bagging Meta-Estimator | Random Forest | Quiz: Random Forest | Hyper-parameters of Random Forest | Quiz: Hyper-parameters of Random Forest | Implementing Random Forest | Introduction to boosting | What is Boosting? | Quiz: Introduction to Boosting | Gradient Boosting Algorithm (GBM) | Math Behind GBM | Quiz: Gradient Boosting Algorithm | Extreme Gradient Boosting (XGBoost) | Implementing XGBoost | Quiz: XGBoost | AdaBoost: Adaptive Boosting | Implementing AdaBoost | Quiz: AdaBoost | LightGBM | CatBoost | Next Steps
            Key Takeaways: 
            FAQs: Q: Who should take the Ensemble Learning and Ensemble Learning Techniques course? A: This course is designed for anyone who wants to understand how Ensemble Learning and the various Ensemble Learning techniques work. This is an important concept in machine learning that you would need to have a good grasp on. | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A:  | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Ensemble Learning and Ensemble Learning Techniques” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Ensemble Learning and Ensemble Learning Techniques” course in a few hours. | Q: I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Appled Machine Learning” course. | Q: Can I download the videos in this course? A: 
            


key: chroma:document
string_value:
            Title: Linear Programming for Data Science Professionals
            Description: Optimization is the way of life. We all have finite resources and time and we want to make the most of them. From using your time productively to solving supply chain problems for your company – everything uses optimization.
And that’s where learning linear programming will make you a better data science professional.
We are solving optimization problems everyday - without realizing it. Think of how you distributed the chocolate among your peers or siblings - that’s your way of optimizing the situation. On the other hand devising inventory and warehousing strategy for an e-tailer can be very complex. Millions of SKUs with different popularity in different regions to be delivered in defined time and resources.
And linear programming helps us solve these optimization problems with ease and efficiency. As a data science professional, you are bound to come across these optimization problems that you will solve using linear programming.
 Simply put, you should know what linear programming is, and the different methods to solve linear programming problems.
            Curriculum: How to Use the Mini-Course Template | AI&ML Blackbelt Plus Program (Sponsored) | Introduction to Linear Programming | What is Linear Programming | Formulating a problem – Let’s manufacture some chocolates | Common Terminologies in Linear Programming | Process to Formulate a Linear Programming Problem | Using Graph: Problem | Using Graph : Solution | Using R Programing: Problem | Using R  Programing: Solution | Using Open-Solver(Excel):Problem | Using Open-Solver(Excel): Solution | Method 1 - Simplex Method(Question) | Simplex method: Solution | Method 2 - Northwest Corner Method(Problem) | Northwest Corner Method: Solution | Method 3 - Least Cost Method | Applications | What Next?
            Key Takeaways: 
            FAQs: Q: Who should take the Introduction to Linear Programming for Data Science Professionals course? A: This course is designed for anyone who wants to understand what linear programming is, how it works, what are the different linear programming problems out there, and how to solve them. This is an under-appreciated topic in data science that will propel your skillset to a entirely different level. | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: Absolutely! This course covers a topic that is not reliant on machine learning knowledge. That’s the beauty of linear programming - you will rely on a bit of math and a programming language or tool (like MS Excel). That’s all you need to learn and solve linear programming problems! | Q: What is the fee for the course? A: This course is free of cost! All you need to do is sign up and get started. | Q: How long would I have access to the “Introduction to Linear Programming for Data Science Professionals” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Introduction to Linear Programming for Data Science Professionals” course in a few hours. | Q: I’ve completed this course and have a good grasp on linear programming. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course. | Q: Can I download the videos in this course? A: We regularly update the “Introduction to Linear Programming for Data Science Professionals” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Naive Bayes from Scratch
            Description: Naive Bayes ranks in the top echelons of the machine learning algorithms pantheon. It is a popular and widely used machine learning algorithm and is often the go-to technique when dealing with classification problems.
The beauty of Naive Bayes lies in it’s incredible speed. You’ll soon see how fast the Naive Bayes algorithm works as compared to other classification algorithms. It works on the Bayes theorem of probability to predict the class of unknown datasets. You’ll learn all about this inside the course!
So whether you’re trying to solve a classic HR analytics problem like predicting who gets promoted, or you’re aiming to predict loan default - the Naive Bayes algorithm will get you on your way.
            Curriculum: Key Terms and Definitions | Introduction to Probability | Quiz: Introduction to probability | Calculating Probabilities of events | Quiz: Calculating Probabilities of events | AI&ML Blackbelt Plus Program (Sponsored) | Introduction to Naive Bayes | Quiz: Introduction to Naive Bayes | Conditional Probability and Bayes Theorem | Working of Naive Bayes | Quiz: Conditional Probability and Naive Bayes | Math Behind Naive Bayes | Types of Naive Bayes | Quiz: Types of Naive Bayes | Implementing Naive Bayes | Pros and Cons of Naive Bayes | Applications of Naive Bayes | Improve your Naive Bayes Model | More Resources and Next Steps
            Key Takeaways: 
            FAQs: Q: Who should take the Naive Bayes course? A: We have designed this course in a way that’s easy to understand for beginners in machine learning. You will get to learn Naive Bayes from scratch and also implement it using Python and R. Take this course if you want to learn a popular machine learning algorithm and expand your repertoire of techniques to solve classification problems. | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: You would need to have an idea of the core machine learning algorithms. Naive Bayes is a slightly advanced algorithm so you would at least need to know what is supervised and unsupervised learning, and the difference between regression and classification problems. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Naive Bayes from Scratch” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enrol in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Naive Bayes from Scratch” course in a few hours. | Q: I’ve completed this course and have a good grasp on Naive Bayes. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course. | Q: Can I download the videos in this course? A: 
            


key: chroma:document
string_value:
            Title: Learn Swift for Data Science
            Description: he Swift programming language is quickly becoming the language of choice for a lot of data science experts and professionals. Swift’s flexibility, ease of use, excellent documentation, and quick execution speed are key reasons behind the language’s recent prominence in the data science space.
Swift is a more efficient, stable and secure programming language as compared to Python. In fact, Swift is also a good language to build for mobile. In fact, it’s the official language for developing iOS applications for the iPhone!
The cherry on the cake for Swift? It has the support of the likes of Google, Apple, and FastAI behind it!
“I always hope that when I start looking at a new language, there will be some mind-opening new ideas to find, and Swift definitely doesn’t disappoint. Swift tries to be expressive, flexible, concise, safe, easy to use, and fast. Most languages compromise significantly in at least one of these areas.” – Jeremy Howard
And when Jeremy Howard endorses a language and starts using it for his daily data science work, you need to drop everything and listen.
In this free course on Swift for Data Science, we will learn about Swift as a programming language and how it fits into the data science space. If you’re a Python user, you’ll notice the subtle differences and the incredible similarities between the two. We showcase Swift code as well in the course so get started!
            Curriculum: Getting Started | Why Swift? | AI&ML Blackbelt Plus Program (Sponsored) | The Swift Ecosystem | Setting up the Environment | Basics of Swift programming - I | Basics of Swift programming - II | Python with Swift | The Swift4Tensorflow Library | About the Dataset and Setup | Implementation of MNIST Image Classification | Introduction | Setting up the system | Basic Text Processing | Language Identification in iOS | Spell Checking and Correction | Part Of Speech (POS) Tagging | Identifying People, Organization, etc. from the Text (Named Entity Recognition) | Performing Sentiment Analysis on iOS | Word Embeddings | Next Steps
            Key Takeaways: 
            FAQs: Q: Who should take the Swift for Data Science course? A: We have curated the Swift for Data Science course for beginners with the Swift programming language. You should take the course if you are interested in broadening your data science skillset, learning a new programming language, and want to add a hot and upcoming tool to your budding data science resume! | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: You would need to have an idea of the core machine learning algorithms. We will guide you on how to use Swift for data science to build machine learning algorithms so you would at least need to know what is supervised and unsupervised learning, and the difference between regression and classification problems. | Q: What is the fee for the course? A: This course is free of cost! | Q: How long would I have access to the “Swift for Data Science” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enrol in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Swift for Data Science” course in a few hours. | Q: I’ve completed this course and have a good grasp on the Swift programming language. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course. | Q: Can I download the videos in this course? A: We regularly update the “Swift for Data Science” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Introduction to Web Scraping using Python
            Description: The need and importance of extracting data from the web is becoming increasingly loud and clear. There is an unprecedented volume of data on the internet right now - and data science projects often need this data to build predictive models.
That’s a key reason why data scientists are expected to be familiar with web scraping.
We have found web scraping to be a very helpful technique for gathering data from multiple websites. Some websites these days also provide APIs for many different types of data you might want to use, such as Tweets or LinkedIn posts.
But there might be occasions when you need to collect data from a website that does not provide a specific API. This is where having the ability to perform web scraping comes in handy. As a data scientist, you can code a simple Python script and extract the data you’re looking for.
So knowing how to perform web scraping using Python will help you go a long way towards becoming a resourceful data scientist. Are you ready to take the next step and dive in?
A note of caution here – web scraping is subject to a lot of guidelines and rules. Not every website allows the user to scrape content so there are certain legal restrictions at play. Always ensure you read the website’s terms and conditions on web scraping before you attempt to do it.
In this course, we will dive into the basics of web scraping using Python. We will understand what web scraping is, the different Python libraries for performing web scraping, and finally we’ll implement web scraping using Python in a real-world project. There’s a lot to unpack here so enroll today and start learning!
            Curriculum: What is Web Scraping? | Caution | Popular Libraries for Web Scraping | Components of Web Scraping | AI&ML Blackbelt Plus Program (Sponsored) | Problem Setup | Step 1: Crawl | Step 2: Parse and Transform | Step 3: Store the Data | Single Webpage Scraping | Multiple Webpage Scraping(BeautifulSoup and Regex) | Scrape Images in Python | Scarpe Data on Page Load
            Key Takeaways: 
            FAQs: Q: Who should take the Introduction to Web Scraping using Python course? A: This course is designed for anyone who wants to learn everything about getting started with web scraping using Python. Web scraping is an incredibly useful tool to have in your data scientist’s armoury and this course will get you started on the right footing. | Q: I have decent programming experience but no background in machine learning. Is this course right for me? A: Absolutely! This course covers a topic that is not reliant on machine learning knowledge. All you need are basic Python programming skills - everything else will fall into place as you go through the contents of the course. | Q: What is the fee for the course? A: This course is free of cost! All you need to do is sign up and get started. | Q: How long would I have access to the “Introduction to Web Scraping using Python” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Introduction to Web Scraping using Python” course in a few hours. | Q: I’ve completed this course and have a good grasp on linear programming. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course. | Q: Can I download the videos in this course? A: We regularly update the “Introduction to Web Scraping using Python” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Tableau for Beginners
            Description: Tableau is the gold standard in business intelligence, analytics and data visualization tools. Tableau Desktop (and now Tableau Public) have transformed the way we interact with visualizations and tell data stories to our clients, stakeholders, and to non-technical audiences around the world.
Tableau has been recognized as a Leader in the Gartner Magic Quadrant for Analytics and Business Intelligence Platforms for 8 straight years. Here’s Gartner’s most recent ranking in 2020:


In this Tableau for Beginners course, you will learn everything you need to get started with this wonderful visualization and business intelligence tool. You’ll be able to build charts like bar charts, line charts (for working with time series data), pie charts, and even get the hang of geospatial analysis using map visualizations in Tableau!
 
Note: If you’re looking to build and master dashboards and storyboards in Tableau, make sure you check out the popular ‘Mastering Tableau from Scratch: Become a Data Visualization Rockstar’ course!
            Curriculum: Welcome to the Course | AI&ML Blackbelt Plus Program (Sponsored) | What is Data Visualization and Why Should we Use it | Hans Rosling - 200 Countries 200 Years 4 Minutes | Navigating the Tableau Interface Part 1 | Navigating the Tableau Interface Part 2 | Connect to the Data | Data Visualizations | Net Statistics | Net Statistics Part 2 | Line Chart | Pie Chart | Map Chart | Scatter Plots | Filters | Trend Line | What's Next?
            Key Takeaways: 
            FAQs: Q: Who should take the Introduction to Tableau for Beginners course? A: This course is for anyone who is curious about Tableau and wants to understand how it works. We have designed the course especially for beginners in Tableau (and data visualization). We even cover the what and why of data visualization before we dive into Tableau - so it’s all from scratch! | Q: I have worked with visualizations but have no background in analytics or business intelligence. Is this course right for me? A: Absolutely! As we mentioned earlier, this is a “from scratch” course. We do not assume any prior knowledge about analytics or business intelligence or Tableau. Our aim is to provide you with the knowledge of how to operate and work with Tableau to build impactful and effective visualizations in the analytics and BI industry. | Q: What is the fee for the course? A: This course is free of cost! All you need to do is sign up and get started with Tableau. | Q: How long would I have access to the “Tableau for Beginners” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort do I need to put in for this course? A: You can complete the “Tableau for Beginners” course in a few hours. | Q: I’ve completed this course and have a good grasp on the basics of Tableau. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Mastering Tableau from Scratch: Become a Data Visualization Rockstar” course. | Q: Can I download the videos in this course? A: We regularly update the “Tableau for Beginners” course and hence do not allow videos to be
downloaded. You can visit the free course anytime to refer to these videos.
            


key: chroma:document
string_value:
            Title: Getting Started with Neural Networks
            Description: Introduction to Neural Networks
What is a neural network? How does it work? What does a neural network do? Learn neural networks for free in this course and get your neural network questions answered, including applications of neural networks in deep learning.

Learn how neural networks work in deep learning
Do you want to acquire a super power? How about learning neural networks? Neural networks are at the heart of the deep learning revolution that’s happening around us right now.
Neural networks are the present and the future. The different neural network architectures like convolutional neural networks (CNN), recurrent neural networks (RNN), and others have altered the deep learning landscape.
But as a beginner in this field, you’ll have a ton of questions:

What is a neural network?
Why do we need to learn neural networks?
How popular are neural networks?
What are the advantages of neural networks?
What kind of challenges you could face when applying neural networks?
What exactly should you learn about neural networks?
What are the core concepts that make up neural networks?
What are the different types of neural networks in deep learning?
Do you need to know programming to build a neural network?
Which programming language is best for building neural networks? Python or R?
What are the different applications of neural networks?
What kind of problems or projects can you solve using neural networks?


From classifying images and translating languages to building a self-driving car, neural networks are powering the world around us. Thanks to the idea of neural networks like CNN and RNN, deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis!
            Curriculum: What is Deep Learning? | Difference b/w Deep Learning and Machine Learning | Why Deep Learning is so popular? | AI&ML Blackbelt Plus Program (Sponsored) | Hardware for Computations in Deep Learning | Setting up your system | Introduction to Google Colab | Understanding Google Colab Interface | Pre-requisites for Deep Learning | Perceptron | Quiz - Perceptron | Weights in Perceptron | Quiz - Weights in Perceptron | Multi Layer Perceptron | Quiz - Multi Layer Perceptron | Forward and Backward Prop Intuition | Quiz - Forward and Backward Prop Intuition | Gradient Descent Algorithm | Quiz - Gradient Descent Algorithm | Why do we need activation functions? | Quiz - Why do need activation functions | Linear Activation Function | Quiz - Linear Activation Function | Sigmoid and tanh | Quiz - Sigmoid and tanh | Softmax | Quiz - Softmax | Introduction to loss function | Quiz - Introduction to Loss Function | Binary and Categorical Cross entropy / log loss | Quiz - Binary and Categorical cross entropy / log loss | Understanding Problem Statement: Loan Prediction | Data Preprocessing: Loan Prediction | Quiz - Data Preprocessing: Loan Prediction | Steps to solve Loan Prediction Challenge | Loading loan prediction dataset | Defining the Model Architecture for loan prediction problem | Training and Evaluating model on Loan Prediction Challenge | Quiz - Training and Evaluating model on Loan Prediction Challenge | Assignment: Big Mart Sales Prediction | Object Detection, segmentation, image generation | Quiz - Object Detection, Segmentation, image generation | Sequential Modeling | Quiz - Sequential Modeling | Test Your Neural Network Skills | Where to go from here?
            Key Takeaways: 
            FAQs: Q: Why do we need to learn neural networks? A: Neural networks are at the core of the majority of deep learning applications. From computer vision use cases like facial recognition and object detection, to Natural Language Processing (NLP) tasks like writing essays and building human-like chatbots, neural networks are ubiquitous.

The different offshoots of neural networks, such as convolutional neural networks (CNN), recurrent neural networks (RNNs), and Long Short Term Memory (LSTM), are a must-know.

Neural networks are the present and the future - you should start learning right now! | Q: Why are neural networks so popular? A: The above deep learning applications must have given you a fairly good idea of how useful neural networks are. They are incredibly powerful and organizations around the world are tapping into neural networks like CNN and RNN to help them find solutions to pressing problems.

Here are a few key reasons why neural networks and deep learning have become mainstream:
●	Cheaper computation cost in recent years means neural networks are accessible to a wider deep learning community
●	Availability of larger datasets means we can truly harness the power of neural networks
●	Neural networks have consistently shown better performance on Computer Vision, Natural Language Processing (NLP), and other key domains | Q: What are the advantages of neural networks? A: Phew! There are a ton of advantages to learning neural networks in the scope of deep learning.
●	Neural networks can handle a huge amount of data. This is something traditional machine learning algorithms struggle with
●	Neural networks have a proven record of improving on the performance of traditional machine learning algorithms
●	Neural networks can implicitly detect complex non-linear relationships between variables
●	Wide range of applications! Neural networks are being used across industries, roles and functions | Q: What kind of challenges could you face when applying neural networks? A: While neural networks like CNN and RNN are inherently awesome and are changing the deep learning field, there are still a few challenges you might face.
●	Computation power: Deep neural networks like CNN and RNN can take up a lot of processing power
●	Interpretability: Deep neural networks might be difficult to interpret or explain
●	Overfitting: Modern neural networks have a tendency of overfitting on training data (but this can be rectified with regularization)
●	Large datasets: Neural networks typically need large amount of data to train on | Q: What exactly should you learn about neural networks? A: This is the million dollar question! What exactly should you learn about neural networks? That’s where this free course by Analytics Vidhya will help you.

We will cover topics like what is a neural network, the different parts of a neural network, applications of a neural network, among other things. We have also baked in exercises after each video lesson to test your neural network knowledge! | Q: What are the core concepts that make up a neural network? A: Excellent question! Here are three key concepts you should know about the workings of a neural network:
●	What is forward propagation?
●	What is backward propagation (backpropagation)?
●	Role of Gradient Descent in neural networks

We will cover each of these topics in the course. | Q: What are the different types of neural networks in deep learning? A: There are different types of neural networks you will come across. Here are a few key ones:
●	Artificial Neural Network (ANN)
●	Recurrent Neural Network (RNN)
●	Convolutional Neural Network (CNN)
●	Long Short Term Memory (LSTM)

We’ve seen CNN and RNN getting a lot of attention lately in the deep learning community. | Q: Which programming language should I learn to build neural networks? A: Python is the preferred language for learning and building neural networks in deep learning. We’ve seen a strong preference towards Python and hence we use it in the course.

If you’re new to Python, we recommend taking our free Python course. | Q: What kind of problems or projects can I solve using neural networks? A: Neural networks open up a whole new world of projects. Once you have a hang of how neural networks work, you can get your hands on a dataset and start solving problems right away.

We recommend heading over to our DataHack Platform and picking up the problem that you find the most interesting or relevant. | Q: Who should take this course? A: This course is for people who want to kick start their deep learning journey and learn what and how neural networks work. | Q: I have a programming experience of 2+ years, but no background in Deep Learning. Is the course right for me? A: You’ve arrived at the perfect place! This course is ideal for beginners in deep learning. You will learn neural networks from scratch, the building block of deep learning. | Q: What is the fee for this course? A: This course is free of cost! | Q: How long would I have access to the “Introduction to Neural Networks” course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: How much effort will this course take? A: You can complete the "Introduction to Neural Networks" course in a few hours. You are also expected to apply your knowledge of neural networks and learning of this course to solve deep learning problems. The time taken in projects varies from person to person. | Q: How can I apply and test my learnings about Neural Networks? A: You can start by doing the tests at the end of the lessons in this course. In addition, you can apply Neural Networks to solve various problems on Analytics Vidhya’s DataHack Platform. | Q: I’ve completed this course already and have a decent knowledge about neural networks. What should I learn next? A: That’s great! We recommend taking the next step in your deep learning journey with the popular “Computer Vision using Deep Learning” course. You will work on real world hands-on computer vision case studies, learn the fundamentals of deep learning, and get familiar with tips and tricks to improve your deep learning models. | Q: . Can I download videos from this course? A: We regularly update the "Introduction to Neural Networks" course and hence do not allow for videos to be downloaded. You can visit this free course anytime to refer to these videos. | Q: Which programming language is used to teach Neural Networks in this course? A: This course uses Python programming language and its open source libraries to teach you how to build neural networks.
            


key: chroma:document
string_value:
            Title: Introduction to AI & ML
            Description: Analytics Vidhya’s ‘Introduction to AI and ML’ course, curated and delivered by experienced instructors with decades of industry experience between them, will help you understand the answers to these pressing questions.
Artificial Intelligence and Machine Learning have become the centerpiece of strategic decision making for organizations. They are disrupting the way industries and roles function - from sales and marketing to finance and HR, companies are betting big on AI and ML to give them a competitive edge.
And this, of course, directly translates to their hiring. Thousands of vacancies are open as organizations scour the world for AI and ML talent. There hasn’t been a better time to get into this field!
            Curriculum: What is AI&ML? | Types of ML | When to Apply AI&ML | Recent AI Uprising | How the world is Changing? | Building Blocks of AI and ML | Knowing Each Other | AI&ML Blackbelt Plus Program (Sponsored) | Common Terminologies | Common Data Capturing Types and Tools | Common Tools | Common Techniques | Common Techniques - Part1 | Common Techniques - Part2 | Skills Required in Data Science | AI and ML Black Belt+ | Where to Go from here!!
            Key Takeaways: You will learn the current state of AI and ML, how they are disrupting businesses globally | Solid understanding of what AI and ML mean, what they represent in the current market and industry, how they work, and why you should learn about them.
            FAQs: Q: I have decent programming experience but no background in AI and ML. Is this course right for me? A: Absolutely! We have designed and taught this course in a way that caters to beginners coming from any background. We assume no prior knowledge about AI or machine learning - we cover everything from scratch in this course. | Q: What is the fee for the course? A: This course is free of cost. | Q: How much effort do I need to put in for this course? A: You’ll be able to finish this course in a week’s time if you spend half an hour on it daily. | Q: I’ve completed this course and have a good grasp on what AI and ML mean. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. There are plenty of options to choose from. We suggest heading to courses.analyticsvidhya.com and browning through the various offerings available. | Q: Can I download the videos in this course? A: We do not allow videos to be downloaded from the platform. You can visit this free course anytime to refer to these videos.
            


key: url
string_value:
https://courses.analyticsvidhya.com/courses/winning-data-science-hackathons-learn-from-elite-data-scientists


key: chroma:document
string_value:
            Title: Winning Data Science Hackathons - Learn from Elite Data Scientists
            Description: There is no substitute for experience. And that holds true in Data Science competitions as well. These cut-throat hackathons require a lot of trial-and-error, effort, and dedication to reach the ranks of the elite.
This course is an amalgamation of various talks by top data scientists and machine learning hackers, experts, practitioners, and leaders who have participated and won dozens of hackathons. They have already gone through the entire learning process and they showcase their work and thought process in these talks. 
This course features top data science hackers and experts, including Sudalai Rajkumar (SRK), Dipanjan Sarkar, Rohan Rao, Kiran R and many more!
From effective feature engineering to choosing the right validation strategy, there is a LOT to learn from this course so get started today!
            Curriculum: About the Winning Data Science Hackathon course | AI&ML Blackbelt Plus Program (Sponsored) | Effective Feature Engineering – A Structured Approach to Building Better ML Models - By Dipanjan Sarkar | Automating the Machine Learning Pipeline with AutoML -By Dr. Sunil Kumar Chinnamgari | Panel Discussion - What Sets the Top Hackers Apart? | Top Hacks from a Kaggle Grandmaster by Pavel Pleskov | Feature Engineering for Image Data by Aishwarya Singh  & Pulkit Sharma
            Key Takeaways: 
            FAQs: Q: Do I need to install any software as part of the course? A: No, you don’t need any software for this course. A working internet connection is enough to get you started! | Q: Do I need to take the modules in a specific order? A: No, you can pick and choose the talks you want to listen to in any order you prefer. | Q: How long can I access the course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: Where can I watch all the videos from DataHack Summit? A: 
            


key: chroma:document
string_value:
            Title: Hypothesis Testing for Data Science and Analytics
            Description: Statistics is the study of the collection, analysis, interpretation, presentation, and organisation of data. For all the data science and machine learning enthusiasts it is paramount to be well versed with various statistical concepts such as Hypothesis testing
Every day we find ourselves testing new ideas, finding the fastest route to the office, the quickest way to finish our work, or simply finding a better way to do something we love. The critical question, then, is whether our idea is significantly better than what we tried previously.
These ideas that we come up with on such a regular basis – that’s essentially what a hypothesis is. And testing these ideas to figure out which one works and which one is best left behind, is called hypothesis testing.
            Curriculum: Introduction to Hypothesis Testing Course | AI&ML Blackbelt Plus Program (Sponsored) | Understanding Hypothesis Testing | Steps to Perform for Hypothesis testing | Critical Value - p-value | Directional Hypothesis | Non-Directional Hypothesis | What is Z test? | One-Sample Z test | One-Sample Z test - Example | Two-Sample Z Test | Two-Sample Z Test - Example | What is t-test? | One-Sample t-Test | One Sample t-Test - Example | Two-Sample t-Test | Two-Sample t-Test - Example | Deciding between Z Test and T-Test | Two-Sample Z test for a Coronavirus Dataset
            Key Takeaways: Fundamentals of Hypothesis Testing: The course begins with a simple-to-understand example on hypothesis testing. This chapter will clear all your basics like -  Null Hypothesis, Alternative Hypothesis, Type 1 Error, Type 2 Error, and Significance Level. | p-value | What is the Z Test? The course introduces the most basic type of testing a hypothesis - Z test. Z tests are a statistical way of testing. The chapter covers the one sample as well as the two sample Z test. | What is the t-Test? The t-test is another test for validating the hypothesis. The chapter begins with a unique example and later covers both the one-sample and two-sample t-test. | Deciding between the Z Test and t-Test  Are you confused on how to use these tests? Don’t worry, the course comprises a simple step-by-step guide on choosing the best test for your experiment. | Case Study: Hypothesis Testing for Coronavirus in Python You’ll get to put your theoretical knowledge into practice and see how well you can do. You will get to work on a hypothesis testing case study on the corona virus dataset in Python!
            FAQs: Q: You’ll get to put your theoretical knowledge into practice and see how well you can do. You will get to work on a hypothesis testing case study on the corona virus dataset in Python! A: A working knowledge of basic statistics would be helpful. Even though we have designed this course for beginners, knowing a bit about basic statistics will help you visualize certain concepts in a more vivid manner. | Q: What is the fee for the course? A: This course is free of cost. | Q: How much effort do I need to put in for this course? A: You’ll be able to finish this course in a week’s time if you spend an hour on it daily and explore it on your own along with what you learn here. | Q: I’ve completed this course and have a good grasp on Hypothesis Testing. What should I learn next? A: The next step in your journey is to build on what you’ve learned so far. There are plenty of options to choose from. We suggest heading to courses.analyticsvidhya.com and browsing through the various offerings available.
You should also incorporate hypothesis testing in your daily life! Try and use problem statements from around you and build hypotheses based on those/ That’s the best way to learn. | Q: Do I need to install any software as part of the course? A: You will not require any software for the learning modules. Python and other libraries are necessary to solve the case study. | Q: How long can I access the course? A: Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration - you will need to enroll in the course again. Your past progress will be lost. | Q: Can I download the videos in this course? A: We do not allow videos to be downloaded from the platform. You can visit this free course anytime to refer to these videos.
            


