# Courses

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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

## Algorithms

- 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

## Artificial Intelligence

## Business

## Chemistry

## Compilers

## Computer Science

- 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

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

## Cryptocurrency

## Cryptography

## CSS

## Decentralized systems

## Deep Learning

- 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

## Discrete math

## Functional programming

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

## Game development

## Haskell

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

## Investing

## iOS

## Machine learning

- 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)

## Math

- 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)

## Networking

## Neuroscience

## Natural Language Processing

## Operating systems

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

## Programming

- 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.

## React

- 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.

## ReasonML

## Rust

## Scala

## Security

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

## Statistics

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

## Swift

## Type theory

## Vim

## Web Development

## Related

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

`Source: GitHub`