Awesome Courses

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The number after the course name stands for the year in which the course was made in. All courses are put in their respective category and are sorted from oldest to newest. If no year was found for the course, it is put in the end of the list.

Contents

• Compilers
• iOS
• Investing
• Security
• Business
• Cryptography
• Web Development
• Game development
• Chemistry
• Operating systems
• Algorithms
• Functional programming
• Math
• Statistics
• Type theory
• Networking
• Neuroscience
• Computer vision
• Artificial Intelligence
• Machine learning
• Computer Science
• Deep Learning
• Haskell
• Rust
• Scala
• Programming
• CSS
• Related

Compilers

• Principles of compiler design (2016)
• Stanford compiler construction (2016)

iOS

• Developing iOS 10 apps with Swift (2017)

Investing

• Computational investing

Security

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

Business

• Gamification

Cryptography

• Stanford cryptography I
• Stanford cryptography II (2017)

Cryptocurrency

• Bitcoin and Cryptocurrency Technologies

Web Development

• Cutting-edge web technologies (2015)
• Interactive Flexbox course (2018)

React

• Advanced React Patterns (2017)
• Beginner's guide to React (2017)
• React Holiday (2017) - React through examples.
• Leverage New Features of React 16 (2018)
• Building React Applications with Redux
• Building React Applications with Idiomatic Redux

Game development

• HTML5 game development

Chemistry

• Bioinformatics specialisation

Operating systems

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

Algorithms

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

Functional programming

• Course in functional programming by KTH
• Functional Programming Course

Vim

• Vim valley

Math

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

Statistics

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

Type theory

• Homotopy Type Theory (2014)

Discrete math

• Discrete Mathematics and Probability Theory

Neuroscience

• The Human Brain (2018)

Networking

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

Computer vision

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

Artificial Intelligence

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

Machine learning

• Learning from data (2012)
• Machine learning for data science (2015)
• Introduction to matrix methods (2015)
• Statistical learning (2015)
• Tensorflow for deep learning research (2017)
• Practical Deep Learning For Coders (2018) - Learn how to build state of the art models without needing graduate-level math.
• Introduction to Deep Learning (2018) - Introductory course on deep learning algorithms and their applications.
• Machine Learning Crash Course (2018) - Google's fast-paced, practical introduction to machine learning.
• Mathematics of Deep Learning, NYU, Spring (2018)
• Coursera machine learning
  • Notes
• Artificial intelligence for robotics
• Neural networks for machine learning
• Machine learning in Python with scikit-learn

Computer Science

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

Deep Learning

• Deep learning at Oxford (2015)
• Ucl reinforcement learning (2015)
  • Lectures
• Oxford cs deep nlp (2017)
• Deep learning (2017)
• Berkeley deep reinforcement learning (2017)
• Stanford natural language processing with deep learning (2017)
• Stanford deep learning for natural language processing
• Stanford convolutional neural networks for visual recognition

Haskell

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

Rust

• Rust programming (2016)

Swift

• Hacking with Swift (2018)

Scala

• Functional programming principles in scala

Programming

• Unix tools and scripting (2014)
• MIT structure and interpretation of computer programs (2005)
• MIT software construction (2016)
• Structure and interpretation of computer programs (in Python) (2017)
• Build a modern computer from first principles: from nand to tetris
• Introduction to programming with matlab
• Stanford C Programming

CSS

• CSS Grid by Wes Bos

Related

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

Contributing

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