- Algorithms
- Artificial Intelligence
- Business
- CSS
- Chemistry
- Compilers
- Computer Science
- Computer vision
- Cryptocurrency
- Cryptography
- Deep Learning
- Discrete math
- Functional programming
- Game development
- Haskell
- Investing
- Machine learning
- Math
- Networking
- Neuroscience
- Operating systems
- Programming
- React
- Related
- Rust
- Scala
- Security
- Statistics
- Swift
- Type theory
- Vim
- Web Development
- iOS
- Algorithmic thinking
- Algorithms (2010) - Taught by Manuel Blum who has a Turing Award due to his contributions to algorithms.
- Algorithms specialisation
- 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
- Lectures
- Computational complexity (2008)
- Computer science 101
- Data structures
- Great ideas in computer architecture (2015)
- Information retrieval (2013)
- MIT Mathematics for Computer Science (2010)
- MIT Structure and Interpretation of Programs (1986)
- MIT great ideas in theoretical computer science
- 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)
- Lectures
- Berkeley deep reinforcement learning (2017)
- Deep learning (2017)
- Deep learning at Oxford (2015)
- Oxford cs deep nlp (2017)
- Stanford convolutional neural networks for visual recognition
- Stanford deep learning for natural language processing
- Stanford natural language processing with deep learning (2017)
- Ucl reinforcement learning (2015)
- Advanced Programming (2017)
- Haskell ITMO (2017)
- Introduction to haskell (2016)
- Stanford functional systems in haskell (2014)
- Notes
- Artificial intelligence for robotics
- Coursera machine learning
- Introduction to Deep Learning (2018) - Introductory course on deep learning algorithms and their applications.
- 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
- Mathematics of Deep Learning, NYU, Spring (2018)
- Neural networks for machine learning
- 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)
- mlcourse.ai - Open Machine Learning course by OpenDataScience.
- Machine Learning with TensorFlow on Google Cloud Platform Specialization - A machine learning specialization taught by Google that includes going through how Google does machine learning. It doesn't just stop at teaching machine learning, it goes through how to integrate ML with existing systems, and how to build production-ready machine learning models.
- Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization - Get hands-on experience optimizing, deploying, and scaling production ML models.
- Abstract algebra (2014)
- 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)
- 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)
- Advanced React Patterns (2017)
- Beginner's guide to React (2017)
- Building React Applications with Idiomatic Redux
- Building React Applications with Redux
- Leverage New Features of React 16 (2018)
- React Holiday (2017) - React through examples.
- Awesome artificial intelligence
- Awesome courses
- CS video courses
- Data science courses
- Dive into machine learning
- 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
