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| title,description,Course curriculum,About the Instructor,combined_text | |
| Coding a ChatGPT-style Language Model from Scratch in PyTorch,"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.","Introduction | |
| Importing | |
| Creating Inputs & Lables | |
| Position Encoding | |
| Masked Self-Attention | |
| Putting Pieces Together | |
| Creating Decoder Only Transformer","About the Instructor | |
| Dr. Joshua Starmer - Co-Founder and CEO of Statsquest | |
| Dr. Joshua Starmer, co-founder and CEO of Statsquest, is a visionary in Artificial Intelligence and Data Science, renowned for transforming complex concepts into practical, actionable insights. With a Ph.D. in Biomathematics and a distinguished career spanning academia and industry, his engaging presentations empower and inspire learners to excel in the ever-evolving world of AI and analytics. | |
| LinkedIn","Coding a ChatGPT-style Language Model from Scratch in PyTorch 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. Introduction | |
| Importing | |
| Creating Inputs & Lables | |
| Position Encoding | |
| Masked Self-Attention | |
| Putting Pieces Together | |
| Creating Decoder Only Transformer About the Instructor | |
| Dr. Joshua Starmer - Co-Founder and CEO of Statsquest | |
| Dr. Joshua Starmer, co-founder and CEO of Statsquest, is a visionary in Artificial Intelligence and Data Science, renowned for transforming complex concepts into practical, actionable insights. With a Ph.D. in Biomathematics and a distinguished career spanning academia and industry, his engaging presentations empower and inspire learners to excel in the ever-evolving world of AI and analytics. | |
| LinkedIn" | |
| Mastering Multilingual GenAI Open-Weights for Indic Languages,"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.","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","About the Instructor | |
| Viraat Aryabumi - Research Scholar at Cohere for AI | |
| Viraat is a Research Scholar at Cohere for AI, where he contributed to the Aya Project and Aya-101 model. He previously led Machine Learning at Aiara and was a Machine Learning Scientist at Amazon. He holds a Master's in AI from the University of Edinburgh. | |
| LinkedIn","Mastering Multilingual GenAI Open-Weights for Indic Languages 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. 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 About the Instructor | |
| Viraat Aryabumi - Research Scholar at Cohere for AI | |
| Viraat is a Research Scholar at Cohere for AI, where he contributed to the Aya Project and Aya-101 model. He previously led Machine Learning at Aiara and was a Machine Learning Scientist at Amazon. He holds a Master's in AI from the University of Edinburgh. | |
| LinkedIn" | |
| Learning Autonomous Driving Behaviors with LLMs & RL,"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.","Introduction | |
| Evolution RL | |
| Understanding RL | |
| Challenges with RL | |
| Approach to the Problem Statement | |
| Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL","About the Instructor | |
| Mayank Baranwal - Senior Scientist at TCS Research | Adjunct Professor at IITB | INAE Young Associate | UIUC | IITK | |
| Mayank Baranwal is a Senior Scientist at Tata Consultancy Services (TCS) Research, Mumbai, and an Adjunct Faculty at IIT Bombay. He holds a PhD in Mechanical Science and Engineering from the University of Illinois, Urbana-Champaign (UIUC). His research focuses on optimization, control, and network systems with applications in supply chains, power networks, and deep learning. He has received several accolades, including the Young Scientist Award (2022) and the Gold Award for Best Smart Technology in Electricity Transmission (2023). | |
| LinkedIn","Learning Autonomous Driving Behaviors with LLMs & RL 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. Introduction | |
| Evolution RL | |
| Understanding RL | |
| Challenges with RL | |
| Approach to the Problem Statement | |
| Hands-On: Learning Autonomous Driving Behaviors with LLMs & RL About the Instructor | |
| Mayank Baranwal - Senior Scientist at TCS Research | Adjunct Professor at IITB | INAE Young Associate | UIUC | IITK | |
| Mayank Baranwal is a Senior Scientist at Tata Consultancy Services (TCS) Research, Mumbai, and an Adjunct Faculty at IIT Bombay. He holds a PhD in Mechanical Science and Engineering from the University of Illinois, Urbana-Champaign (UIUC). His research focuses on optimization, control, and network systems with applications in supply chains, power networks, and deep learning. He has received several accolades, including the Young Scientist Award (2022) and the Gold Award for Best Smart Technology in Electricity Transmission (2023). | |
| LinkedIn" | |
| GenAI Applied to Quantitative Finance: For Control Implementation,"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.","Introduction | |
| Overview | |
| Problem Definition: Commodity Price Prediction | |
| Architecture | |
| Hands-On",No instructor available,"GenAI Applied to Quantitative Finance: For Control Implementation 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. Introduction | |
| Overview | |
| Problem Definition: Commodity Price Prediction | |
| Architecture | |
| Hands-On No instructor available" | |
| "Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy","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.","Introduction | |
| Resources | |
| Technique to Increase Accuracy | |
| Training Speed and Cost Optimization | |
| Inference Speed and Cost Optimization | |
| Scale","Instructor | |
| Kartik Nighania - MLOps Engineer at Typewise|Certified AWS Cloud and Kubernetes Engineer | |
| Kartik Nighania, an MLOps Engineer at Typewise, brings over seven years of AI experience across computer vision, NLP, and DevOps. Formerly Head of Engineering at Pibit.ai, he led AI-driven automation and infrastructure scaling. His expertise in CI/CD pipelines was honed at HSBC Technology, and his academic work includes AI publications and projects like ML-driven crop health detection. | |
| LinkedIn","Navigating LLM Tradeoffs: Techniques for Speed, Cost, Scale & Accuracy 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. Introduction | |
| Resources | |
| Technique to Increase Accuracy | |
| Training Speed and Cost Optimization | |
| Inference Speed and Cost Optimization | |
| Scale Instructor | |
| Kartik Nighania - MLOps Engineer at Typewise|Certified AWS Cloud and Kubernetes Engineer | |
| Kartik Nighania, an MLOps Engineer at Typewise, brings over seven years of AI experience across computer vision, NLP, and DevOps. Formerly Head of Engineering at Pibit.ai, he led AI-driven automation and infrastructure scaling. His expertise in CI/CD pipelines was honed at HSBC Technology, and his academic work includes AI publications and projects like ML-driven crop health detection. | |
| LinkedIn" | |
| Creating Problem-Solving Agents using GenAI for Action Composition,"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.","Introduction | |
| Overview- Count the Number of Agents | |
| A brief history of Agentic Systems | |
| Agents Today | |
| Multi-Agent Systems Today | |
| Practical Solutions","About the Instructor | |
| Vikas Agrawal - Senior Principal Data Scientist at Oracle Analytics Cloud | |
| Vikas Agrawal is a Senior Principal Data Scientist at Oracle Analytics Cloud, focused on designing and deploying AI solutions across ERP, SCM, HCM, CX, and MFG for Fusion and NetSuite customers. He ensures that AI models are automatically generated, updated, and tailored to each customer's evolving data. His research involves developing intelligent agents that leverage domain knowledge to solve complex problems by combining tools and critiqued LLMs/LMMs for hypothesis generation and task orchestration. An electrical engineer turned computer scientist, Vikas is an IIT Delhi graduate with experience at CalTech, Intel, and Infosys. | |
| LinkedIn","Creating Problem-Solving Agents using GenAI for Action Composition 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. Introduction | |
| Overview- Count the Number of Agents | |
| A brief history of Agentic Systems | |
| Agents Today | |
| Multi-Agent Systems Today | |
| Practical Solutions About the Instructor | |
| Vikas Agrawal - Senior Principal Data Scientist at Oracle Analytics Cloud | |
| Vikas Agrawal is a Senior Principal Data Scientist at Oracle Analytics Cloud, focused on designing and deploying AI solutions across ERP, SCM, HCM, CX, and MFG for Fusion and NetSuite customers. He ensures that AI models are automatically generated, updated, and tailored to each customer's evolving data. His research involves developing intelligent agents that leverage domain knowledge to solve complex problems by combining tools and critiqued LLMs/LMMs for hypothesis generation and task orchestration. An electrical engineer turned computer scientist, Vikas is an IIT Delhi graduate with experience at CalTech, Intel, and Infosys. | |
| LinkedIn" | |
| Improving Real World RAG Systems: Key Challenges & Practical Solutions,"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.","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","About the Instructor | |
| Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya | |
| Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include ""Top 10 Data Scientists in India, 2020,"" ""40 under 40 Data Scientists, 2021,"" ""Google Developer Expert in Machine Learning, 2019,"" and ""Top 50 AI Thought Leaders, Global AI Hub, 2022,"" alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022. | |
| LinkedIn","Improving Real World RAG Systems: Key Challenges & Practical Solutions 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. 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 About the Instructor | |
| Dipanjan Sarkar - Head of Community and Principal AI Scientist, Analytics Vidhya | |
| Dipanjan Sarkar is a distinguished Lead Data Scientist, Published Author, and Consultant, having a decade of extensive expertise in Machine Learning, Deep Learning, Generative AI, Computer Vision, and Natural Language Processing. His leadership spans Fortune 100 enterprises to startups, crafting end-to-end data products and pioneering Generative AI upskilling programs. A seasoned mentor, Dipanjan advises a diverse clientele, from novices to C-suite executives and PhDs, across Advanced Analytics, Product Development, and Artificial Intelligence. His recognitions include ""Top 10 Data Scientists in India, 2020,"" ""40 under 40 Data Scientists, 2021,"" ""Google Developer Expert in Machine Learning, 2019,"" and ""Top 50 AI Thought Leaders, Global AI Hub, 2022,"" alongside global accolades and a Google Champion Innovator title in Cloud AI/ML, 2022. | |
| LinkedIn" | |
| Framework to Choose the Right LLM for your Business,"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.",Introduction,"About the Instructor | |
| Rohan Rao - Principal Data Scientist, H2O.ai; Quadruple Kaggle Grandmaster | |
| A Principal Data Scientist at H2O.ai and an IIT-Bombay alumnus, is a highly accomplished professional. He is a quadruple Kaggle Grandmaster and was formerly ranked #1 on AnalyticsVidhya. In addition to his expertise in data science, Rohan is a 9-time National Sudoku Champion. A versatile individual, he is also a passionate coder, reader, writer, and lifelong learner, known in the community as ""vopani."" | |
| LinkedIn","Framework to Choose the Right LLM for your Business 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. Introduction About the Instructor | |
| Rohan Rao - Principal Data Scientist, H2O.ai; Quadruple Kaggle Grandmaster | |
| A Principal Data Scientist at H2O.ai and an IIT-Bombay alumnus, is a highly accomplished professional. He is a quadruple Kaggle Grandmaster and was formerly ranked #1 on AnalyticsVidhya. In addition to his expertise in data science, Rohan is a 9-time National Sudoku Champion. A versatile individual, he is also a passionate coder, reader, writer, and lifelong learner, known in the community as ""vopani."" | |
| LinkedIn" | |
| Building Smarter LLMs with Mamba and State Space Model,"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.",Course Overview,"About the Instructor | |
| Maarten Grootendorst - Senior Clinical Data Scientist, IKNL; Creator of KeyBERT and BERTopic | |
| Marteen holds three master’s degrees in Organizational, Clinical Psychology, and Data Science, using them to simplify machine learning for a broad audience. As co-author of Hands-On Large Language Models and through popular blogs, he’s reached millions by explaining AI, often from a psychological lens. He’s also the creator of widely-used open-source packages like BERTopic, PolyFuzz, and KeyBERT, which have millions of downloads and are utilized by data professionals globally.","Building Smarter LLMs with Mamba and State Space Model 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. Course Overview About the Instructor | |
| Maarten Grootendorst - Senior Clinical Data Scientist, IKNL; Creator of KeyBERT and BERTopic | |
| Marteen holds three master’s degrees in Organizational, Clinical Psychology, and Data Science, using them to simplify machine learning for a broad audience. As co-author of Hands-On Large Language Models and through popular blogs, he’s reached millions by explaining AI, often from a psychological lens. He’s also the creator of widely-used open-source packages like BERTopic, PolyFuzz, and KeyBERT, which have millions of downloads and are utilized by data professionals globally." | |
| Generative AI - A Way of Life - Free Course,"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.","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!",No instructor available,"Generative AI - A Way of Life - Free Course 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. 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! No instructor available" | |
| Building LLM Applications using Prompt Engineering - Free Course,"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.","Introduction to Building Different LLM applications | |
| Prompt Engineering | |
| Retrieval Augmented Generation | |
| Finetuning LLMs | |
| Training LLMs from Scratch | |
| Quiz",No instructor available,"Building LLM Applications using Prompt Engineering - Free Course 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. Introduction to Building Different LLM applications | |
| Prompt Engineering | |
| Retrieval Augmented Generation | |
| Finetuning LLMs | |
| Training LLMs from Scratch | |
| Quiz No instructor available" | |
| Building Your First Computer Vision Model - Free Course,"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.","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",No instructor available,"Building Your First Computer Vision Model - Free Course 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. 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 No instructor available" | |
| Bagging and Boosting ML Algorithms - Free Course,"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","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",No instructor available,"Bagging and Boosting ML Algorithms - Free Course 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 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 | |
| Introduction to Random Forest | |
| Understanding Out-Of-Bag Score | |
| Random Forest VS Classical Bagging VS Decision Tree | |
| Project",No instructor available,"Bagging and Boosting ML Algorithms - Free Course 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 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 No instructor available" | |
| MidJourney: From Inspiration to Implementation - Free Course,"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.","MidJourney - Storm _ Story | |
| MidJourney - Inspiration | |
| MidJourney - How to use | |
| MidJourney Alternatives | |
| Quiz",No instructor available,"MidJourney: From Inspiration to Implementation - Free Course 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. MidJourney - Storm _ Story | |
| MidJourney - Inspiration | |
| MidJourney - How to use | |
| MidJourney Alternatives | |
| Quiz No instructor available" | |
| Understanding Linear Regression - Free Course,"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.","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",No instructor available,"Understanding Linear Regression - Free Course 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. 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 No instructor available" | |
| The Working of Neural Networks - Free Course,"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.","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",No instructor available,"The Working of Neural Networks - Free Course 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. 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 No instructor available" | |
| The A to Z of Unsupervised ML - Free Course,"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.","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",No instructor available,"The A to Z of Unsupervised ML - Free Course 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. 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 No instructor available" | |
| Building Your first RAG System using LlamaIndex - Free Course,"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.","Welcome to this course | |
| Why RAG | |
| What is RAG system | |
| Overview of RAG Framework | |
| Quiz | |
| Course handouts",No instructor available,"Building Your first RAG System using LlamaIndex - Free Course 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. Welcome to this course | |
| Why RAG | |
| What is RAG system | |
| Overview of RAG Framework | |
| Quiz | |
| Course handouts No instructor available" | |
| Data Preprocessing on a Real-World Problem Statement - Free Course,"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.","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",No instructor available,"Data Preprocessing on a Real-World Problem Statement - Free Course 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. 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 No instructor available" | |
| Exploring Stability.AI - Free Course,"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","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",No instructor available,"Exploring Stability.AI - Free Course 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 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 No instructor available" | |
| Building a Text Classification Model with Natural Language Processing - Free Course,"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.","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",No instructor available,"Building a Text Classification Model with Natural Language Processing - Free Course 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. 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 No instructor available" | |
| Getting Started with Large Language Models,"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.","Course Objective | |
| Course Handouts | |
| The Exponential Growth",No instructor available,"Getting Started with Large Language Models 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. Course Objective | |
| Course Handouts | |
| The Exponential Growth No instructor available" | |
| Introduction to Generative AI,"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.","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!",No instructor available,"Introduction to Generative AI 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. 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! No instructor available" | |
| Nano Course: Dreambooth-Stable Diffusion for Custom Images,"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.","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",No instructor available,"Nano Course: Dreambooth-Stable Diffusion for Custom Images 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. 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 No instructor available" | |
| A Comprehensive Learning Path for Deep Learning in 2023,"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.","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)",No instructor available,"A Comprehensive Learning Path for Deep Learning in 2023 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. 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) No instructor available" | |
| A Comprehensive Learning Path to Become a Data Scientist in 2024,"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.","Overview of Learning Path | |
| Month-on-Month Plan | |
| Your Personalized Learning Path for Data Science | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"A Comprehensive Learning Path to Become a Data Scientist in 2024 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. Overview of Learning Path | |
| Month-on-Month Plan | |
| Your Personalized Learning Path for Data Science | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Nano Course: Building Large Language Models for Code,"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","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",No instructor available,"Nano Course: Building Large Language Models for Code 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 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 No instructor available" | |
| Certified AI & ML BlackBelt+ Program,"What happens when you combine ALL of Analytics Vidhya’s comprehensive courses, curated and designed by instructors with decades of data science experience? You get the AI & ML BlackBelt+ program! | |
| There are multiple elements that go into becoming an AI expert. Data Science, Machine Learning and Deep Learning are the core components you would need in our journey to break into the wonderful world of AI applications. | |
| AI & ML BlackBelt+ is a thoughtfully curated program designed for anyone wanting to learn data science, machine learning, deep learning in their quest to become an AI professional. It all starts here, so are you ready to take the ride? | |
| You will get access to ALL the courses Analytics Vidhya has curated and designed as part of AI & ML Blackbelt+. What are you waiting for? Start your AI journey today!",No curriculum available,No instructor available,"Certified AI & ML BlackBelt+ Program What happens when you combine ALL of Analytics Vidhya’s comprehensive courses, curated and designed by instructors with decades of data science experience? You get the AI & ML BlackBelt+ program! | |
| There are multiple elements that go into becoming an AI expert. Data Science, Machine Learning and Deep Learning are the core components you would need in our journey to break into the wonderful world of AI applications. | |
| AI & ML BlackBelt+ is a thoughtfully curated program designed for anyone wanting to learn data science, machine learning, deep learning in their quest to become an AI professional. It all starts here, so are you ready to take the ride? | |
| You will get access to ALL the courses Analytics Vidhya has curated and designed as part of AI & ML Blackbelt+. What are you waiting for? Start your AI journey today! No curriculum available No instructor available" | |
| Machine Learning Summer Training,"This is the second step of the Machine Learning Summer Training, want to know more click here.","Overview of the Course | |
| FREE PREVIEW | |
| Knowing each other | |
| FREE PREVIEW | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Machine Learning Summer Training This is the second step of the Machine Learning Summer Training, want to know more click here. Overview of the Course | |
| FREE PREVIEW | |
| Knowing each other | |
| FREE PREVIEW | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| AI Ethics by Fractal,"AI has a huge influence on our lives. From typing on our smartphones, to personalized recommendations on our favourite shopping websites, intelligent machines are everywhere. Our interactions with technology have become more personalized, but with humans ultimately behind these creations, the question is: where does the responsibility lie? Why and how should we begin the AI ethics conversation at Fractal? | |
| Learning plan: | |
| The video course is followed by MCQ test to gauge the depth of your understanding and help you retain your learning. | |
| Learners can take the e-learning and complete the MCQ Test activity post viewing the video.","Introduction and need for Ethical AI | |
| FREE PREVIEW | |
| Fractal's Ethical AI Principles | |
| Framework: Behaviors and toolkits overview | |
| Next Steps & Further Learning | |
| Test Your Self",No instructor available,"AI Ethics by Fractal AI has a huge influence on our lives. From typing on our smartphones, to personalized recommendations on our favourite shopping websites, intelligent machines are everywhere. Our interactions with technology have become more personalized, but with humans ultimately behind these creations, the question is: where does the responsibility lie? Why and how should we begin the AI ethics conversation at Fractal? | |
| Learning plan: | |
| The video course is followed by MCQ test to gauge the depth of your understanding and help you retain your learning. | |
| Learners can take the e-learning and complete the MCQ Test activity post viewing the video. Introduction and need for Ethical AI | |
| FREE PREVIEW | |
| Fractal's Ethical AI Principles | |
| Framework: Behaviors and toolkits overview | |
| Next Steps & Further Learning | |
| Test Your Self No instructor available" | |
| A Comprehensive Learning Path to Become a Data Engineer in 2022,"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.","Overview of Learning Path | |
| Month-on-Month Plan | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"A Comprehensive Learning Path to Become a Data Engineer in 2022 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. Overview of Learning Path | |
| Month-on-Month Plan | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Certified Business Analytics Program,"With increase in data generated across the globe, the demand for Business Analytics professionals is rising continuously. | |
| In short, aiming for a role in Business Analytics has never been a better career choice! | |
| Certified Business Analytics Program aims to provide you all the tools and techniques, along with hands on experience you need to succeed as a Business Analytics professional. | |
| This program covers tools like Excel, Tableau, SQL, Python and covers all the techniques like Statistics and Exploratory Data Analysis. The program also covers Predictive modeling and basics of Machine Learning. | |
| More importantly, the program helps you prepare your Resume, prepares you for Business Analytics Interviews and provides one on one mentorship during the program.",No curriculum available,No instructor available,"Certified Business Analytics Program With increase in data generated across the globe, the demand for Business Analytics professionals is rising continuously. | |
| In short, aiming for a role in Business Analytics has never been a better career choice! | |
| Certified Business Analytics Program aims to provide you all the tools and techniques, along with hands on experience you need to succeed as a Business Analytics professional. | |
| This program covers tools like Excel, Tableau, SQL, Python and covers all the techniques like Statistics and Exploratory Data Analysis. The program also covers Predictive modeling and basics of Machine Learning. | |
| More importantly, the program helps you prepare your Resume, prepares you for Business Analytics Interviews and provides one on one mentorship during the program. No curriculum available No instructor available" | |
| Certified Machine Learning Master's Program (MLMP),"NYC Taxi Trip Duration Prediction | |
| Customer Churn Prediction | |
| Web Page Classification | |
| Malaria Detection from blood cell Images | |
| Predict Survivors from Titanic | |
| Sales Prediction for Large Super Markets | |
| Movie Recommender System | |
| Article Recommender System | |
| Online Book Recommender System | |
| Market Basket Analysis for a Super Market | |
| Forecasting the daily count of Airlines booking using historical data | |
| Using Time series models for forecasting energy consumption | |
| Forecasting web Traffic using Deep Learning",No curriculum available,No instructor available,"Certified Machine Learning Master's Program (MLMP) NYC Taxi Trip Duration Prediction | |
| Customer Churn Prediction | |
| Web Page Classification | |
| Malaria Detection from blood cell Images | |
| Predict Survivors from Titanic | |
| Sales Prediction for Large Super Markets | |
| Movie Recommender System | |
| Article Recommender System | |
| Online Book Recommender System | |
| Market Basket Analysis for a Super Market | |
| Forecasting the daily count of Airlines booking using historical data | |
| Using Time series models for forecasting energy consumption | |
| Forecasting web Traffic using Deep Learning No curriculum available No instructor available" | |
| Certified Natural Language Processing Master’s Program,"Natural Language Processing (NLP) is one of the fastest growing field within Artificial Intelligence. It enables machines to understand information contained in any text. | |
| NLP bridges the gap between the abstract but omnipresent human languages with concise and concrete programming languages. As more and more organizations invest in data - they would need NLP experts. | |
| This comprehensive program empowers you to become an NLP practitioner, even if you are a beginner and have no prior knowledge of the concepts. | |
| Download Brochure",No curriculum available,No instructor available,"Certified Natural Language Processing Master’s Program Natural Language Processing (NLP) is one of the fastest growing field within Artificial Intelligence. It enables machines to understand information contained in any text. | |
| NLP bridges the gap between the abstract but omnipresent human languages with concise and concrete programming languages. As more and more organizations invest in data - they would need NLP experts. | |
| This comprehensive program empowers you to become an NLP practitioner, even if you are a beginner and have no prior knowledge of the concepts. | |
| Download Brochure No curriculum available No instructor available" | |
| Certified Computer Vision Master's Program,"We have designed this certified program for Computer Vision enthusiasts like you who are looking for a place to start. Computer Vision is currently among the hottest fields in the industry. The demand for computer vision experts is outstripping the supply! So you’ve picked the perfect time to get into this field. This comprehensive program powers you to become a computer vision expert. The beauty of this program is that it assumes no prior knowledge of concepts. We start from the ground up by learning the basics of Python, statistics, core machine learning algorithms & fundamentals of Deep Learning. | |
| Once your base is rock solid, jump over to the Computer Vision using Deep Learning course. It is designed to give you a taste of how the underlying techniques work in current State-of-the-Art Computer Vision systems, and walks you through remarkable Computer Vision applications in a hands-on manner so that you can create such solutions on your own. | |
| Object detection | |
| Face detection | |
| Image Classification | |
| Image Segmentation | |
| Image generation, and many others! | |
| You can then combine your technical knowledge with the learning from the Ace Data Science Interviews course to land your dream job in data science & computer vision! The program consists of five comprehensive and rich courses curated exclusively by Analytics Vidhya. | |
| Download Brochure",No curriculum available,No instructor available,"Certified Computer Vision Master's Program We have designed this certified program for Computer Vision enthusiasts like you who are looking for a place to start. Computer Vision is currently among the hottest fields in the industry. The demand for computer vision experts is outstripping the supply! So you’ve picked the perfect time to get into this field. This comprehensive program powers you to become a computer vision expert. The beauty of this program is that it assumes no prior knowledge of concepts. We start from the ground up by learning the basics of Python, statistics, core machine learning algorithms & fundamentals of Deep Learning. | |
| Once your base is rock solid, jump over to the Computer Vision using Deep Learning course. It is designed to give you a taste of how the underlying techniques work in current State-of-the-Art Computer Vision systems, and walks you through remarkable Computer Vision applications in a hands-on manner so that you can create such solutions on your own. | |
| Object detection | |
| Face detection | |
| Image Classification | |
| Image Segmentation | |
| Image generation, and many others! | |
| You can then combine your technical knowledge with the learning from the Ace Data Science Interviews course to land your dream job in data science & computer vision! The program consists of five comprehensive and rich courses curated exclusively by Analytics Vidhya. | |
| Download Brochure No curriculum available No instructor available" | |
| Applied Machine Learning - Beginner to Professional,"Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally. It is no longer a buzzword - many different industries have already seen automation of business processes and disruptions from Machine Learning. In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems. | |
| Pre-requisites for the Applied Machine Learning course | |
| This course requires no prior knowledge about Data Science or any tool. | |
| Download Brochure","Overview of Machine Learning / Data Science | |
| FREE PREVIEW | |
| Common Terminology used in Data Science | |
| FREE PREVIEW | |
| Applications of Data Science | |
| FREE PREVIEW",No instructor available,"Applied Machine Learning - Beginner to Professional Machine Learning is re-shaping and revolutionising the world and disrupting industries and job functions globally. It is no longer a buzzword - many different industries have already seen automation of business processes and disruptions from Machine Learning. In this age of machine learning, every aspiring data scientist is expected to upskill themselves in machine learning techniques & tools and apply them in real-world business problems. | |
| Pre-requisites for the Applied Machine Learning course | |
| This course requires no prior knowledge about Data Science or any tool. | |
| Download Brochure Overview of Machine Learning / Data Science | |
| FREE PREVIEW | |
| Common Terminology used in Data Science | |
| FREE PREVIEW | |
| Applications of Data Science | |
| FREE PREVIEW No instructor available" | |
| Ace Data Science Interviews,"Are you trying to get into data science roles but getting rejected by employers? Are you scared of getting into data science interviews? Or don't know what to expect in data science interviews? This is just the course you need. | |
| While you might know the tools and techniques in data science, clearing a data science interview might still prove very difficult. You need to show your problem solving skills and technical prowess in these data science interviews. | |
| This course has been created based on hundreds of interviews we have taken, companies we have helped in data science interviews and several data science experts in the industry. | |
| Key learnings and takeaways from ""Ace Data Science Interviews"" course: | |
| Understand different roles existing in data science ecosystem(e.g.Data Scientist, Data Engineers, Data Analyst etc.) | |
| Learn what skill sets required for each of these roles | |
| Understand different types of Interviews which happen in Data Science Industry | |
| Tips and tricks to Ace your Data Science Interviews | |
| How to build your digital presence including LinkedIn and GitHub profile | |
| Learn the process to create a professional experience for data science roles. | |
| Framework to solve Guesstimates and case studies used in data science interviews | |
| Downloadable Resources: | |
| Infographic for 7 step process to ""Ace Data Science Interviews"" | |
| e-book containing more than 240 interview questions from interviews in industry. | |
| Interview Questions on machine learning, statistics, Model building, Machine Learning production, SQL | |
| Checklist for your LinkedIn and GitHub profiles","Instructor Introduction | |
| FREE PREVIEW | |
| Why did we create this course | |
| FREE PREVIEW | |
| How did we create this Course | |
| FREE PREVIEW | |
| Who should take this course | |
| FREE PREVIEW",No instructor available,"Ace Data Science Interviews Are you trying to get into data science roles but getting rejected by employers? Are you scared of getting into data science interviews? Or don't know what to expect in data science interviews? This is just the course you need. | |
| While you might know the tools and techniques in data science, clearing a data science interview might still prove very difficult. You need to show your problem solving skills and technical prowess in these data science interviews. | |
| This course has been created based on hundreds of interviews we have taken, companies we have helped in data science interviews and several data science experts in the industry. | |
| Key learnings and takeaways from ""Ace Data Science Interviews"" course: | |
| Understand different roles existing in data science ecosystem(e.g.Data Scientist, Data Engineers, Data Analyst etc.) | |
| Learn what skill sets required for each of these roles | |
| Understand different types of Interviews which happen in Data Science Industry | |
| Tips and tricks to Ace your Data Science Interviews | |
| How to build your digital presence including LinkedIn and GitHub profile | |
| Learn the process to create a professional experience for data science roles. | |
| Framework to solve Guesstimates and case studies used in data science interviews | |
| Downloadable Resources: | |
| Infographic for 7 step process to ""Ace Data Science Interviews"" | |
| e-book containing more than 240 interview questions from interviews in industry. | |
| Interview Questions on machine learning, statistics, Model building, Machine Learning production, SQL | |
| Checklist for your LinkedIn and GitHub profiles Instructor Introduction | |
| FREE PREVIEW | |
| Why did we create this course | |
| FREE PREVIEW | |
| How did we create this Course | |
| FREE PREVIEW | |
| Who should take this course | |
| FREE PREVIEW No instructor available" | |
| Writing Powerful Data Science Articles,"""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!","Why did we create this course? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Writing Powerful Data Science Articles ""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! Why did we create this course? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Machine Learning Certification Course for Beginners,"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.","Overview of the Course | |
| FREE PREVIEW | |
| Knowing each other | |
| FREE PREVIEW | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Machine Learning Certification Course for Beginners 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. Overview of the Course | |
| FREE PREVIEW | |
| Knowing each other | |
| FREE PREVIEW | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Data Science Career Conclave,"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?","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)",No instructor available,"Data Science Career Conclave 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? 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) No instructor available" | |
| Top Data Science Projects for Analysts and Data Scientists,"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!","About the Data Science Projects Course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Top Data Science Projects for Analysts and Data Scientists 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! About the Data Science Projects Course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Getting Started with Git and GitHub for Data Science Professionals,"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!","What is Git? | |
| What is Github? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Getting Started with Git and GitHub for Data Science Professionals 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! What is Git? | |
| What is Github? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Machine Learning Starter Program,"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.",No curriculum available,No instructor available,"Machine Learning Starter Program 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. No curriculum available No instructor available" | |
| "Data Science Hacks, Tips and Tricks","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!","About the Data Science Hacks, Tips and Tricks Course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Data Science Hacks, Tips and Tricks 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! About the Data Science Hacks, Tips and Tricks Course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Introduction to Business Analytics,"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","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)",No instructor available,"Introduction to Business Analytics 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 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) No instructor available" | |
| Introduction to PyTorch for Deep Learning,"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.","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)",No instructor available,"Introduction to PyTorch for Deep Learning 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. 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) No instructor available" | |
| Introductory Data Science for Business Managers,"The data science revolution is well and truly disrupting multiple and diverse industries. It has become the centerpiece of strategic decision making for organizations. Are you prepared to enable it for your business and your current role? | |
| There is a serious shortage of decision-makers in the data science universe. While projects are springing up everywhere, the managers and leaders required to guide and mentor data science teams are rare to find. | |
| Data Science for Managers is a thoughtfully curated program designed especially for decision-makers. Whether you’re a manager, team leader, CxO or entrepreneur, you NEED to be data science educated. And this is the perfect program to get you there. | |
| You will get access to three of the most comprehensive courses in this certified program. Enable yourself, and your business, to be ready for the Artificial Intelligence revolution! | |
| Key Takeaways from Certified Program: Introductory Data Science for Business Managers | |
| Artificial Intelligence and Machine Learning for Business Leaders: The ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs | |
| Introduction to Data Science: Every data science manager and leader should have a good hold on core data science techniques. This course will teach you the basics of the most popular data science language – Python, the basics of core statistics, and introduce you to the essential machine learning algorithms used in businesses today | |
| Tableau 2.0 - Master Tableau from Scratch: Convert your data into actionable insights, create dashboards to impress your clients, and learn Tableau tips, tricks, and best practices for your analytics, business intelligence or data science role! | |
| Why you should take this certified program? | |
| Upskill yourself for the AI Revolution: Artificial Intelligence has already started making a huge impact in various industries, roles and functions. The time to upskill yourself and become familiar with artificial intelligence and machine learning is NOW. This comprehensive program will enable you to do just that. | |
| Easy to understand content: Understanding data science concepts can be difficult. Especially if you’re a mid or late-career transitioner coming from a non-technical background. That’s why all the courses in this program have been curated and designed for people from all walks of life. We don’t assume anything – this is data science from scratch. | |
| Experienced Instructors: All the material in this program was created by instructors who bring immense industry experience of data science. Combined among us, we have more than two decades of teaching experience. | |
| Industry Relevant: All the courses in this program have been vetted by industry experts. This ensures relevance in the industry and enables you with the content which matters most. | |
| Real life problems: All projects in the program are modelled on real-world scenarios. We mean it when we say “industry relevant”! | |
| Prerequisites of Certified Program: Introductory Data Science for Business Managers | |
| This program requires no past knowledge about Data Science or any tool.",No curriculum available,No instructor available,"Introductory Data Science for Business Managers The data science revolution is well and truly disrupting multiple and diverse industries. It has become the centerpiece of strategic decision making for organizations. Are you prepared to enable it for your business and your current role? | |
| There is a serious shortage of decision-makers in the data science universe. While projects are springing up everywhere, the managers and leaders required to guide and mentor data science teams are rare to find. | |
| Data Science for Managers is a thoughtfully curated program designed especially for decision-makers. Whether you’re a manager, team leader, CxO or entrepreneur, you NEED to be data science educated. And this is the perfect program to get you there. | |
| You will get access to three of the most comprehensive courses in this certified program. Enable yourself, and your business, to be ready for the Artificial Intelligence revolution! | |
| Key Takeaways from Certified Program: Introductory Data Science for Business Managers | |
| Artificial Intelligence and Machine Learning for Business Leaders: The ultimate Artificial Intelligence & Machine Learning course for CxOs, Managers, Team Leaders and Entrepreneurs | |
| Introduction to Data Science: Every data science manager and leader should have a good hold on core data science techniques. This course will teach you the basics of the most popular data science language – Python, the basics of core statistics, and introduce you to the essential machine learning algorithms used in businesses today | |
| Tableau 2.0 - Master Tableau from Scratch: Convert your data into actionable insights, create dashboards to impress your clients, and learn Tableau tips, tricks, and best practices for your analytics, business intelligence or data science role! | |
| Why you should take this certified program? | |
| Upskill yourself for the AI Revolution: Artificial Intelligence has already started making a huge impact in various industries, roles and functions. The time to upskill yourself and become familiar with artificial intelligence and machine learning is NOW. This comprehensive program will enable you to do just that. | |
| Easy to understand content: Understanding data science concepts can be difficult. Especially if you’re a mid or late-career transitioner coming from a non-technical background. That’s why all the courses in this program have been curated and designed for people from all walks of life. We don’t assume anything – this is data science from scratch. | |
| Experienced Instructors: All the material in this program was created by instructors who bring immense industry experience of data science. Combined among us, we have more than two decades of teaching experience. | |
| Industry Relevant: All the courses in this program have been vetted by industry experts. This ensures relevance in the industry and enables you with the content which matters most. | |
| Real life problems: All projects in the program are modelled on real-world scenarios. We mean it when we say “industry relevant”! | |
| Prerequisites of Certified Program: Introductory Data Science for Business Managers | |
| This program requires no past knowledge about Data Science or any tool. No curriculum available No instructor available" | |
| Introduction to Natural Language Processing,"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.","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)",No instructor available,"Introduction to Natural Language Processing 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. 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) No instructor available" | |
| Getting started with Decision Trees,"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.","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)",No instructor available,"Getting started with Decision Trees 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. 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) No instructor available" | |
| Introduction to Python,"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","Overview of the Course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Introduction to Python 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 Overview of the Course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Loan Prediction Practice Problem (Using Python),"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","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)",No instructor available,"Loan Prediction Practice Problem (Using Python) 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 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) No instructor available" | |
| Big Mart Sales Prediction Using R,"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.","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)",No instructor available,"Big Mart Sales Prediction Using R 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. 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) No instructor available" | |
| Twitter Sentiment Analysis,"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.","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)",No instructor available,"Twitter Sentiment Analysis 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. 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) No instructor available" | |
| Pandas for Data Analysis in Python,"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.","Introduction to the Course | |
| Pandas Installation | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Pandas for Data Analysis in Python 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. Introduction to the Course | |
| Pandas Installation | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Support Vector Machine (SVM) in Python and R,"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.","What are Support Vector Machines? | |
| Why do we use SVM and how is it better? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Support Vector Machine (SVM) in Python and R 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. What are Support Vector Machines? | |
| Why do we use SVM and how is it better? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Evaluation Metrics for Machine Learning Models,"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!","Types of Machine Learning | |
| Why do we need Evaluation Metrics? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Evaluation Metrics for Machine Learning Models 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! Types of Machine Learning | |
| Why do we need Evaluation Metrics? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Fundamentals of Regression Analysis,"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.",Welcome!,No instructor available,"Fundamentals of Regression Analysis 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. Welcome! No instructor available" | |
| Getting Started with scikit-learn (sklearn) for Machine Learning,"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.",Welcome to this course,No instructor available,"Getting Started with scikit-learn (sklearn) for Machine Learning 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. Welcome to this course No instructor available" | |
| Convolutional Neural Networks (CNN) from Scratch,"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!","What is a Neural Network? | |
| Types of Neural Networks | |
| Prerequisites | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Convolutional Neural Networks (CNN) from Scratch 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! What is a Neural Network? | |
| Types of Neural Networks | |
| Prerequisites | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Dimensionality Reduction for Machine Learning,"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!","Introduction | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Dimensionality Reduction for Machine Learning 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! Introduction | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| K-Nearest Neighbors (KNN) Algorithm in Python and R,"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.",Welcome to the Course,No instructor available,"K-Nearest Neighbors (KNN) Algorithm in Python and R 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. Welcome to the Course No instructor available" | |
| Ensemble Learning and Ensemble Learning Techniques,"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!","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)",No instructor available,"Ensemble Learning and Ensemble Learning Techniques 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! 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) No instructor available" | |
| Linear Programming for Data Science Professionals,"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.","How to Use the Mini-Course Template | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Linear Programming for Data Science Professionals 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. How to Use the Mini-Course Template | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Naive Bayes from Scratch,"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.","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)",No instructor available,"Naive Bayes from Scratch 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. 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) No instructor available" | |
| Learn Swift for Data Science,"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!","Getting Started | |
| Why Swift? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Learn Swift for Data Science 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! Getting Started | |
| Why Swift? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Introduction to Web Scraping using Python,"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!","What is Web Scraping? | |
| Caution | |
| Popular Libraries for Web Scraping | |
| Components of Web Scraping | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Introduction to Web Scraping using Python 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! What is Web Scraping? | |
| Caution | |
| Popular Libraries for Web Scraping | |
| Components of Web Scraping | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Tableau for Beginners,"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!","Welcome to the Course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Tableau for Beginners 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! Welcome to the Course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Getting Started with Neural Networks,"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!","What is Deep Learning? | |
| Difference b/w Deep Learning and Machine Learning | |
| Why Deep Learning is so popular? | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Getting Started with Neural Networks 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! What is Deep Learning? | |
| Difference b/w Deep Learning and Machine Learning | |
| Why Deep Learning is so popular? | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Introduction to AI & ML,"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!","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)",No instructor available,"Introduction to AI & ML 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! 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) No instructor available" | |
| Winning Data Science Hackathons - Learn from Elite Data Scientists,"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!","About the Winning Data Science Hackathon course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Winning Data Science Hackathons - Learn from Elite Data Scientists 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! About the Winning Data Science Hackathon course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" | |
| Hypothesis Testing for Data Science and Analytics,"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.","Introduction to Hypothesis Testing Course | |
| AI&ML Blackbelt Plus Program (Sponsored)",No instructor available,"Hypothesis Testing for Data Science and Analytics 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. Introduction to Hypothesis Testing Course | |
| AI&ML Blackbelt Plus Program (Sponsored) No instructor available" |