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- Algorithmic thinking 💰
- Algorithms (2010) - Taught by Manuel Blum who has a Turing Award due to his contributions to algorithms. 🆓
- Algorithms specialization
- Algorithms: Part 1 🆓
- Algorithms: Part 2 🆓
- Data structures (2016) 🆓
- Data structures (2017) 🆓
- Design and analysis of algorithms (2012) 🆓
- Evolutionary computation (2014) 🆓
- Introduction to programming contests (2012) 🆓
- MIT advanced data structures (2014) 🆓
- MIT introduction to algorithms 🆓
- Computational complexity (2008) 🆓
- Computer science 101 🆓
- Data structures 💰
- Great ideas in computer architecture (2015) 🆓
- Information retrieval (2013) 🆓
- MIT great ideas in theoretical computer science 🆓
- MIT Mathematics for Computer Science (2010) 🆓
- MIT Structure and Interpretation of Programs (1986) 🆓
- Quantum Information Science II: Efficient Quantum Computing - fault tolerance and complexity (2018) 🆓
- Software foundations (2014) 🆓
- The art of recursion (2012) 🆓
- Computer vision 🆓
- Introduction to computer vision (2015) 🆓
- Programming computer vision with python (2012) 🆓
- Advanced Deep Learning & Reinforcement Learning (2018) 🆓
- Berkeley deep reinforcement learning (2017) 🆓
- Deep learning (2017) 🆓
- Stanford natural language processing with deep learning (2017) 🆓
- Deep learning at Oxford (2015) 🆓
- Lectures 🆓
- Oxford CS Deep NLP (2017) 🆓
- Ucl reinforcement learning (2015)
- Stanford convolutional neural networks for visual recognition 🆓
- Stanford deep learning for natural language processing 🆓
- Course in functional programming by KTH 🆓
- Functional Programming Course 🆓
- Programming paradigms (2018) 🆓
- Functional Programming in OCaml (2019)
- Advanced Programming (2017) 🆓
- Haskell ITMO (2017) 🆓
- Introduction to Haskell (2016) 🆓
- Stanford functional systems in Haskell (2014) 🆓
- MIT Deep Learning (2019)
- Amazon’s Machine Learning University course (2018) 🆓
- Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization - Get hands-on experience optimizing, deploying, and scaling production ML models. 💰
- Artificial intelligence for robotics 🆓
- Coursera machine learning 💰
- Introduction to Deep Learning (2018) - Introductory course on deep learning algorithms and their applications. 🆓
- Introduction to Machine Learning for Coders - The course covers the most important practical foundations for modern machine learning. 🆓
- Introduction to matrix methods (2015) 🆓
- Learning from data (2012) 🆓
- Machine Learning Crash Course (2018) - Google's fast-paced, practical introduction to machine learning. 🆓
- Machine learning for data science (2015) 🆓
- Machine learning in Python with scikit-learn 🆓
- Machine Learning with TensorFlow on Google Cloud Platform Specialization - Learn ML with Google Cloud. Real-world experimentation with end-to-end ML. 💰
- Mathematics of Deep Learning, NYU, Spring (2018) 🆓
- mlcourse.ai - Open Machine Learning course by OpenDataScience. 🆓
- Neural networks for machine learning 💰
- Notes 🆓
- Practical Deep Learning For Coders (2018) - Learn how to build state of the art models without needing graduate-level math. 🆓
- Statistical learning (2015) 🆓
- Tensorflow for deep learning research (2017) 🆓
- Abstract algebra (2019) 🆓
- MIT linear algebra (2010) 🆓
- MIT multivariable calculus (2007) 🆓
- MIT multivariable calculus by Prof. Denis Auroux 🆓
- MIT multivariable control systems (2004) 🆓
- MIT singlevariable calculus (2006) 🆓
- Nonlinear dynamics and chaos (2014) 🆓
- Stanford mathematical foundations of computing (2016) 🆓
- Types, Logic, and Verification (2013)
- Introduction to computer networking 🆓
- Introduction to EECS II: digital communication systems (2012) 🆓
- Computer Science 162 🆓
- Computer science from the bottom up 🆓
- How to make a computer operating system (2015) 🆓
- Operating system engineering 🆓
- Build a modern computer from first principles: from nand to tetris 💰
- Introduction to programming with matlab 💰
- MIT software construction (2016) 🆓
- MIT structure and interpretation of computer programs (2005) 🆓
- Stanford C Programming 🆓
- Structure and interpretation of computer programs (in Python) (2017) 🆓
- Unix tools and scripting (2014) 🆓
- Composing Programs - Free online introduction to programming and computer science.
- Advanced React Patterns (2017) 🆓
- Beginner's guide to React (2017) 🆓
- Survive JS: React, From apprentice to master 🆓
- Building React Applications with Idiomatic Redux 🆓
- Building React Applications with Redux 🆓
- Complete intro to React v4 (2018) 🆓
- Leverage New Features of React 16 (2018) 🆓
- React Holiday (2017) - React through examples. 🆓
- Computer and network security (2013) 🆓
- Hacker101 (2018) - Free class for web security. 🆓
- Introduction to probability - the science of uncertainty 🆓
- MIT probabilistic systems analysis and applied probability (2010) 🆓
- Statistical Learning (2016) 🆓
- Statistics 110 🆓
- Awesome artificial intelligence 🆓
- Awesome courses 🆓
- CS video courses 🆓
- Data science courses 🆓
- Dive into machine learning 🆓
