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- Algorithms
- Artificial Intelligence
- Business
- Chemistry
- Compilers
- Computer Science
- Computer vision
- Cryptocurrency
- Cryptography
- CSS
- Decentralized systems
- Deep Learning
- Discrete math
- Functional programming
- Game development
- Haskell
- Investing
- iOS
- Machine learning
- Math
- Networking
- Neuroscience
- Natural Language Processing
- Operating systems
- Programming
- React
- ReasonML
- Rust
- Scala
- Security
- Statistics
- Swift
- Type theory
- Vim
- Web Development
- Related

- Algorithmic thinking π°
- Algorithms (2010) - Taught by Manuel Blum who has a Turing Award due to his contributions to algorithms. π
- Algorithms specialization
- Algorithms: Part 1 π
- Algorithms: Part 2 π
- Data structures (2016) π
- Data structures (2017) π
- Design and analysis of algorithms (2012) π
- Evolutionary computation (2014) π
- Introduction to programming contests (2012) π
- MIT advanced data structures (2014) π
- MIT introduction to algorithms π

- Berkeley intro to ai (2014) π
- MIT artificial intelligence (2010) π
- The society of mind (2011) π

- Gamification π°

- Computational complexity (2008) π
- Computer science 101 π
- Data structures π°
- Great ideas in computer architecture (2015) π
- Information retrieval (2013) π
- MIT great ideas in theoretical computer science π
- MIT Mathematics for Computer Science (2010) π
- MIT Structure and Interpretation of Programs (1986) π
- Quantum Information Science II: Efficient Quantum Computing - fault tolerance and complexity (2018) π
- Software foundations (2014) π
- The art of recursion (2012) π

- Computer vision π
- Introduction to computer vision (2015) π
- Programming computer vision with python (2012) π

- CSS Grid by Wes Bos π

- Advanced Deep Learning & Reinforcement Learning (2018) π
- Berkeley deep reinforcement learning (2017) π
- Deep learning (2017) π
- Stanford natural language processing with deep learning (2017) π
- Deep learning at Oxford (2015) π
- Lectures π
- Oxford CS Deep NLP (2017) π
- Ucl reinforcement learning (2015)
- Stanford convolutional neural networks for visual recognition π
- Stanford deep learning for natural language processing π

- Course in functional programming by KTH π
- Functional Programming Course π
- Programming paradigms (2018) π
- Functional Programming in OCaml (2019)

- Advanced Programming (2017) π
- Haskell ITMO (2017) π
- Introduction to Haskell (2016) π
- Stanford functional systems in Haskell (2014) π

- MIT Deep Learning (2019)
- Amazonβs Machine Learning University course (2018) π
- Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization - Get hands-on experience optimizing, deploying, and scaling production ML models. π°
- Artificial intelligence for robotics π
- Coursera machine learning π°
- Introduction to Deep Learning (2018) - Introductory course on deep learning algorithms and their applications. π
- Introduction to Machine Learning for Coders - The course covers the most important practical foundations for modern machine learning. π
- Introduction to matrix methods (2015) π
- Learning from data (2012) π
- Machine Learning Crash Course (2018) - Google's fast-paced, practical introduction to machine learning. π
- Machine learning for data science (2015) π
- Machine learning in Python with scikit-learn π
- Machine Learning with TensorFlow on Google Cloud Platform Specialization - Learn ML with Google Cloud. Real-world experimentation with end-to-end ML. π°
- Mathematics of Deep Learning, NYU, Spring (2018) π
- mlcourse.ai - Open Machine Learning course by OpenDataScience. π
- Neural networks for machine learning π°
- Notes π
- Practical Deep Learning For Coders (2018) - Learn how to build state of the art models without needing graduate-level math. π
- Statistical learning (2015) π
- Tensorflow for deep learning research (2017) π

- Abstract algebra (2019) π
- MIT linear algebra (2010) π
- MIT multivariable calculus (2007) π
- MIT multivariable calculus by Prof. Denis Auroux π
- MIT multivariable control systems (2004) π
- MIT singlevariable calculus (2006) π
- Nonlinear dynamics and chaos (2014) π
- Stanford mathematical foundations of computing (2016) π
- Types, Logic, and Verification (2013)

- Introduction to computer networking π
- Introduction to EECS II: digital communication systems (2012) π

- Computer Science 162 π
- Computer science from the bottom up π
- How to make a computer operating system (2015) π
- Operating system engineering π

- Build a modern computer from first principles: from nand to tetris π°
- Introduction to programming with matlab π°
- MIT software construction (2016) π
- MIT structure and interpretation of computer programs (2005) π
- Stanford C Programming π
- Structure and interpretation of computer programs (in Python) (2017) π
- Unix tools and scripting (2014) π
- Composing Programs - Free online introduction to programming and computer science.

- Advanced React Patterns (2017) π
- Beginner's guide to React (2017) π
- Survive JS: React, From apprentice to master π
- Building React Applications with Idiomatic Redux π
- Building React Applications with Redux π
- Complete intro to React v4 (2018) π
- Leverage New Features of React 16 (2018) π
- React Holiday (2017) - React through examples. π

- Computer and network security (2013) π
- Hacker101 (2018) - Free class for web security. π

- Introduction to probability - the science of uncertainty π
- MIT probabilistic systems analysis and applied probability (2010) π
- Statistical Learning (2016) π
- Statistics 110 π

- Vim valley π°

- Awesome artificial intelligence π
- Awesome courses π
- CS video courses π
- Data science courses π
- Dive into machine learning π