Resources

• Learning with Big Messy Data - on experience on real-world datasets. |
• Probabilistic Graphical Models
• Introduction to Machine Learning
• Introduction to Data-Centric AI - world machine learning performance with this first-ever Data-Centric AI course from MIT. |
• Computational Thinking & Data Science - solving. Suitable for beginners and experienced learners. |
• Probabilistic Graphical Models - world applications in machine learning, computer vision, and natural language processing. |
• Markov Chains & Algorithmic Applications - world problem-solving. |
• Foundations of Reinforcement Learning - difference learning. |
• Intro to Machine Learning - on experience using Python. |
• Data Science
• Python and Machine Learning
• Statistical Learning with Python - on Python implementations and emphasis on theory and practical applications. |
• Data Science Foundations - world relevance. |
• Data Science
• Analyzing Big Data with Twitter - on experience in leveraging Twitter data to uncover insights and trends. Explore data analysis techniques, collaborate on real-world projects, and develop valuable data science skills. |
• Machine Learning Part 1a
• Machine Learning - on exercises, real-world case studies, and practical applications. |
• Reinforcement Learning - making problems. Covers core principles, algorithms, and real-world applications. |
• Introduction to Machine Learning for Coders - world problems. |
• Machine Learning for Engineers 2022 - world engineering problems with this comprehensive course covering theory, applications, and hands-on projects. |
• Introduction to Pattern Recognition & Machine Learning - world applications. Hands-on experience with Python and popular libraries. |
• Reinforcement Learning - world applications in AI, robotics, and more. |
• Natural Language Processing - on coding assignments. |
• Structural Analysis & Visualization of Networks - world applications. Hands-on labs using Python's NetworkX. |
• Intro to Computing - on activities and team-based learning. |
• Introduction to Problem Solving and Programming - solving, data types, control structures, functions, and object-oriented programming. Suitable for beginners with no prior experience. |
• Introduction to Computer Science and Programming in Python
• Introduction to Computational Thinking - world problems with this interactive MIT online course. |
• Electrical Engineering & Computer Science I
• Introduction to Programming with Arcade Games
• Structure & Interpretation of Computer Programs
• Spatial Data Science - on experience using cutting-edge software and programming languages. |
• CS50's Web Programming with Python & JavaScript - on experience in building and deploying web applications. |