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https://github.com/learn-anything/courses

Awesome Courses
https://github.com/learn-anything/courses
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Awesome Courses
Host: GitHub
URL: https://github.com/learn-anything/courses
Owner: learn-anything
Created: 2017-06-05T20:05:54.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2022-02-24T12:08:44.000Z (almost 3 years ago)
Last Synced: 2024-05-23T08:03:35.629Z (8 months ago)
Topics: awesome, awesome-list, courses, education, learning, list, resources
Homepage:
Size: 190 KB
Stars: 1,054
Watchers: 53
Forks: 181
Open Issues: 2
Metadata Files:
- Readme: readme.md
- Contributing: contributing.md
Awesome Lists containing this project

awesome-github - Awesome Courses
curated-lists - Courses
jimsghstars - learn-anything/courses - Awesome Courses (Others)
ultimate-awesome - courses - Awesome Courses. (Other Lists / Monkey C Lists)
README

        # Courses [![Lists](https://img.shields.io/badge/-more%20lists-0a0a0a.svg?style=flat&colorA=0a0a0a)](https://github.com/learn-anything/curated-lists)

_Please read [contribution guidelines](contributing.md) before contributing._

- [Algorithms](#algorithms)

- [Artificial Intelligence](#artificial-intelligence)

- [Business](#business)

- [Chemistry](#chemistry)

- [Compilers](#compilers)

- [Computer Science](#computer-science)

- [Computer vision](#computer-vision)

- [Cryptocurrency](#cryptocurrency)

- [Cryptography](#cryptography)

- [CSS](#css)

- [Decentralized systems](#decentralized-systems)

- [Deep Learning](#deep-learning)

- [Discrete math](#discrete-math)

- [Functional programming](#functional-programming)

- [Game development](#game-development)

- [Haskell](#haskell)

- [Investing](#investing)

- [iOS](#ios)

- [Machine learning](#machine-learning)

- [Math](#math)

- [Networking](#networking)

- [Neuroscience](#neuroscience)

- [Natural Language Processing](#natural-language-processing)

- [Operating systems](#operating-systems)

- [Programming](#programming)

- [React](#react)

- [ReasonML](#reasonml)

- [Rust](#rust)

- [Scala](#scala)

- [Security](#security)

- [Statistics](#statistics)

- [Swift](#swift)

- [Type theory](#type-theory)

- [Vim](#vim)

- [Web Development](#web-development)

- [Related](#related)

## Algorithms

- [Algorithmic thinking](https://www.coursera.org/learn/algorithmic-thinking-1) 💰

- [Algorithms (2010)](http://www.cs.cmu.edu/afs/cs/academic/class/15451-f10/www/) - Taught by Manuel Blum who has a Turing Award due to his contributions to algorithms. 🆓

- [Algorithms specialization](https://www.coursera.org/specializations/algorithms)

- [Algorithms: Part 1](https://www.coursera.org/learn/algorithms-part1/home/welcome) 🆓

- [Algorithms: Part 2](https://www.coursera.org/learn/algorithms-part2) 🆓

- [Data structures (2016)](http://datastructur.es/sp16/) 🆓

- [Data structures (2017)](http://datastructur.es/sp17/) 🆓

- [Design and analysis of algorithms (2012)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2012/) 🆓

- [Evolutionary computation (2014)](https://courses2.cit.cornell.edu/cs5724/) 🆓

- [Introduction to programming contests (2012)](http://web.stanford.edu/class/cs97si/) 🆓

- [MIT advanced data structures (2014)](http://courses.csail.mit.edu/6.851/spring14/index.html) 🆓

- [MIT introduction to algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/) 🆓

## Artificial Intelligence

- [Berkeley intro to ai (2014)](http://ai.berkeley.edu/home.html) 🆓

- [MIT artificial intelligence (2010)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/lecture-videos/) 🆓

- [The society of mind (2011)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-868j-the-society-of-mind-fall-2011/index.htm) 🆓

## Business

- [Gamification](https://www.coursera.org/learn/gamification) 💰

## Chemistry

- [Bioinformatics specialization](https://www.coursera.org/specializations/bioinformatics) 💰

## Compilers

- [Principles of compiler design (2016)](https://www.cs.swarthmore.edu/%7Ejpolitz/cs75/s16/s_schedule.html) 🆓

- [Stanford compiler construction (2016)](https://web.stanford.edu/class/cs143/) 🆓

## Computer Science

- [Computational complexity (2008)](https://people.eecs.berkeley.edu/~luca/cs278-08/) 🆓

- [Computer science 101](https://lagunita.stanford.edu/courses/Engineering/CS101/Summer2014/about) 🆓

- [Data structures](https://www.coursera.org/learn/data-structures) 💰

- [Great ideas in computer architecture (2015)](http://www-inst.eecs.berkeley.edu/%7Ecs61c/sp15/) 🆓

- [Information retrieval (2013)](http://www.cs.cornell.edu/courses/cs4300/2013fa/) 🆓

- [MIT great ideas in theoretical computer science](https://stellar.mit.edu/S/course/6/sp15/6.045/materials.html) 🆓

- [MIT Mathematics for Computer Science (2010)](https://www.youtube.com/playlist?list=PLB7540DEDD482705B) 🆓

- [MIT Structure and Interpretation of Programs (1986)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-001-structure-and-interpretation-of-computer-programs-spring-2005/video-lectures/) 🆓

- [Quantum Information Science II: Efficient Quantum Computing - fault tolerance and complexity (2018)](https://www.edx.org/course/efficient-quantum-computing-fault-tolerance-and-complexity) 🆓

- [Software foundations (2014)](http://www.seas.upenn.edu/%7Ecis500/cis500-f14/index.html) 🆓

- [The art of recursion (2012)](http://www.cis.upenn.edu/~cis39903/) 🆓

## Computer vision

- [Computer vision](http://crcv.ucf.edu/courses/CAP5415/) 🆓

- [Introduction to computer vision (2015)](http://www.cs.cornell.edu/courses/cs4670/2015sp/lectures/lectures.html) 🆓

- [Programming computer vision with python (2012)](http://programmingcomputervision.com/) 🆓

## Cryptocurrency

- [Bitcoin and Cryptocurrency Technologies](https://www.coursera.org/learn/cryptocurrency) 🆓

## Cryptography

- [Stanford cryptography I](https://www.coursera.org/learn/crypto) 💰

- [Stanford cryptography II (2017)](https://www.coursera.org/learn/crypto2) 💰

## CSS

- [CSS Grid by Wes Bos](https://github.com/wesbos/css-grid) 🆓

## Decentralized systems

- [MIT Decentralized Applications (2018)](http://nil.lcs.mit.edu/6.S974/papers.html) 🆓

## Deep Learning

- [Advanced Deep Learning & Reinforcement Learning (2018)](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs) 🆓

- [Berkeley deep reinforcement learning (2017)](http://rll.berkeley.edu/deeprlcourse/) 🆓

- [Deep learning (2017)](http://deeplearning.cs.cmu.edu/) 🆓

- [Stanford natural language processing with deep learning (2017)](https://www.youtube.com/watch?v=OQQ-W_63UgQ&list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) 🆓

- [Deep learning at Oxford (2015)](https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu) 🆓

- [Lectures](https://www.youtube.com/watch?v=2pWv7GOvuf0&feature=youtu.be&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT) 🆓

- [Oxford CS Deep NLP (2017)](https://github.com/oxford-cs-deepnlp-2017/lectures) 🆓

- [Ucl reinforcement learning (2015)](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)

- [Stanford convolutional neural networks for visual recognition](http://cs231n.stanford.edu/syllabus.html) 🆓

- [Stanford deep learning for natural language processing](http://cs224d.stanford.edu/syllabus.html) 🆓

## Discrete math

- [Discrete Mathematics and Probability Theory](http://www-inst.eecs.berkeley.edu/%7Ecs70/archives.html) 🆓

## Functional programming

- [Course in functional programming by KTH](https://github.com/ID1019/functional-programming) 🆓

- [Functional Programming Course](https://github.com/data61/fp-course) 🆓

- [Programming paradigms (2018)](http://www.cs.nott.ac.uk/~pszgmh/pgp.html) 🆓

- [Functional Programming in OCaml (2019)](http://www.cs.cornell.edu/courses/cs3110/2019sp/textbook/)

## Game development

- [HTML5 game development](https://www.udacity.com/course/html5-game-development--cs255) 🆓

## Haskell

- [Advanced Programming (2017)](https://www.seas.upenn.edu/~cis552/current/index.html) 🆓

- [Haskell ITMO (2017)](https://github.com/jagajaga/FP-Course-ITMO) 🆓

- [Introduction to Haskell (2016)](http://www.seas.upenn.edu/%7Ecis194/spring13/) 🆓

- [Stanford functional systems in Haskell (2014)](http://www.scs.stanford.edu/14sp-cs240h/) 🆓

## Investing

- [Computational investing](https://www.coursera.org/learn/computational-investing) 💰

## iOS

- [Developing iOS 10 apps with Swift (2017)](https://itunes.apple.com/us/course/developing-ios-10-apps-with-swift/id1198467120) 🆓

## Machine learning

- [MIT Deep Learning (2019)](https://github.com/lexfridman/mit-deep-learning)

- [Amazon’s Machine Learning University course (2018)](https://aws.amazon.com/blogs/machine-learning/amazons-own-machine-learning-university-now-available-to-all-developers/) 🆓

- [Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization](https://www.coursera.org/specializations/advanced-machine-learning-tensorflow-gcp) - Get hands-on experience optimizing, deploying, and scaling production ML models. 💰

- [Artificial intelligence for robotics](https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373) 🆓

- [Coursera machine learning](https://www.coursera.org/learn/machine-learning) 💰

- [Introduction to Deep Learning (2018)](http://introtodeeplearning.com/) - Introductory course on deep learning algorithms and their applications. 🆓

- [Introduction to Machine Learning for Coders](http://course.fast.ai/ml.html) - The course covers the most important practical foundations for modern machine learning. 🆓

- [Introduction to matrix methods (2015)](http://stanford.edu/class/ee103/) 🆓

- [Learning from data (2012)](https://work.caltech.edu/telecourse.html) 🆓

- [Machine Learning Crash Course (2018)](https://developers.google.com/machine-learning/crash-course/) - Google's fast-paced, practical introduction to machine learning. 🆓

- [Machine learning for data science (2015)](http://www.cs.cornell.edu/courses/cs4786/2015sp/index.htm) 🆓

- [Machine learning in Python with scikit-learn](https://github.com/justmarkham/scikit-learn-videos) 🆓

- [Machine Learning with TensorFlow on Google Cloud Platform Specialization](https://www.coursera.org/specializations/machine-learning-tensorflow-gcp) - Learn ML with Google Cloud. Real-world experimentation with end-to-end ML. 💰

- [Mathematics of Deep Learning, NYU, Spring (2018)](https://joanbruna.github.io/MathsDL-spring18/) 🆓

- [mlcourse.ai](http://mlcourse.ai) - Open Machine Learning course by OpenDataScience. 🆓

- [Neural networks for machine learning](https://www.coursera.org/learn/neural-networks) 💰

- [Notes](https://github.com/1094401996/machine-learning-coursera) 🆓

- [Practical Deep Learning For Coders (2018)](http://course.fast.ai/) - Learn how to build state of the art models without needing graduate-level math. 🆓

- [Statistical learning (2015)](https://lagunita.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about) 🆓

- [Tensorflow for deep learning research (2017)](http://web.stanford.edu/class/cs20si/index.html) 🆓

## Math

- [Abstract algebra (2019)](https://www.math.upenn.edu/~ted/502F19//math502.html) 🆓

- [MIT linear algebra (2010)](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/) 🆓

- [MIT multivariable calculus (2007)](https://ocw.mit.edu/courses/mathematics/18-02-multivariable-calculus-fall-2007/) 🆓

- [MIT multivariable calculus by Prof. Denis Auroux](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm) 🆓

- [MIT multivariable control systems (2004)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-245-multivariable-control-systems-spring-2004/) 🆓

- [MIT singlevariable calculus (2006)](https://ocw.mit.edu/courses/mathematics/18-01-single-variable-calculus-fall-2006/) 🆓

- [Nonlinear dynamics and chaos (2014)](https://www.youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V) 🆓

- [Stanford mathematical foundations of computing (2016)](http://web.stanford.edu/class/cs103/) 🆓

- [Types, Logic, and Verification (2013)](https://www.fcs.uoregon.edu/research/summerschool/summer13/curriculum.html)

## Networking

- [Introduction to computer networking](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about) 🆓

- [Introduction to EECS II: digital communication systems (2012)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-02-introduction-to-eecs-ii-digital-communication-systems-fall-2012/index.htm) 🆓

## Neuroscience

- [The Human Brain (2018)](https://nancysbraintalks.mit.edu/course/9-11-the-human-brain) 🆓

## Natural Language Processing

- [YSDA Natural Language Processing course (2018)](https://github.com/yandexdataschool/nlp_course) 🆓

## Operating systems

- [Computer Science 162](https://www.youtube.com/watch?v=feAOZuID1HM&list=PLggtecHMfYHA7j2rF7nZFgnepu_uPuYws) 🆓

- [Computer science from the bottom up](http://www.bottomupcs.com/) 🆓

- [How to make a computer operating system (2015)](https://github.com/SamyPesse/How-to-Make-a-Computer-Operating-System) 🆓

- [Operating system engineering](https://pdos.csail.mit.edu/6.828/2016/schedule.html) 🆓

## Programming

- [Build a modern computer from first principles: from nand to tetris ](https://www.coursera.org/learn/build-a-computer) 💰

- [Introduction to programming with matlab](https://www.coursera.org/learn/matlab) 💰

- [MIT software construction (2016)](http://web.mit.edu/6.005/www/fa16/) 🆓

- [MIT structure and interpretation of computer programs (2005)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-001-structure-and-interpretation-of-computer-programs-spring-2005/index.htm) 🆓

- [Stanford C Programming](https://www.youtube.com/playlist?list=PLjn3WmBeabPOUzxcCkzk4jYMGRZMZ6ylF&app=desktop) 🆓

- [Structure and interpretation of computer programs (in Python) (2017)](https://cs61a.org/) 🆓

- [Unix tools and scripting (2014)](http://www.cs.cornell.edu/courses/cs2043/2014sp/) 🆓

- [Composing Programs](https://composingprograms.com/) - Free online introduction to programming and computer science.

## React

- [Advanced React Patterns (2017)](https://github.com/kentcdodds/advanced-react-patterns) 🆓

- [Beginner's guide to React (2017)](https://egghead.io/courses/the-beginner-s-guide-to-react) 🆓

- [Survive JS: React, From apprentice to master](https://survivejs.com/react/introduction/) 🆓

- [Building React Applications with Idiomatic Redux](https://egghead.io/courses/building-react-applications-with-idiomatic-redux) 🆓

- [Building React Applications with Redux](https://egghead.io/courses/building-react-applications-with-idiomatic-redux) 🆓

- [Complete intro to React v4 (2018)](https://btholt.github.io/complete-intro-to-react-v4/) 🆓

- [Leverage New Features of React 16 (2018)](https://egghead.io/courses/leverage-new-features-of-react-16) 🆓

- [React Holiday (2017)](https://react.holiday/) - React through examples. 🆓

## ReasonML

- [Get Started with Reason (2018)](https://egghead.io/courses/get-started-with-reason) 🆓

## Rust

- [Rust programming (2016)](http://cis198-2016s.github.io/) 🆓

## Scala

- [Functional programming principles in scala](https://www.coursera.org/learn/progfun1) 💰

## Security

- [Computer and network security (2013)](https://courseware.stanford.edu/pg/courses/lectures/349991) 🆓

- [Hacker101 (2018)](https://github.com/Hacker0x01/hacker101) - Free class for web security. 🆓

## Statistics

- [Introduction to probability - the science of uncertainty](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) 🆓

- [MIT probabilistic systems analysis and applied probability (2010)](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/index.htm) 🆓

- [Statistical Learning (2016)](https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about) 🆓

- [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=EC2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) 🆓

## Swift

- [Hacking with Swift (2018)](https://www.hackingwithswift.com/read) 🆓

## Type theory

- [Homotopy Type Theory (2014)](https://www.cs.cmu.edu/%7Erwh/courses/hott/) 🆓

## Vim

- [Vim valley](https://vimvalley.com/) 💰

## Web Development

- [Cutting-edge web technologies (2015)](http://inst.eecs.berkeley.edu/%7Ecs294-101/sp15/) 🆓

- [Interactive Flexbox course (2018)](https://scrimba.com/g/gflexbox) 🆓

## Related

- [Awesome artificial intelligence](https://github.com/owainlewis/awesome-artificial-intelligence) 🆓

- [Awesome courses](https://github.com/prakhar1989/awesome-courses) 🆓

- [CS video courses](https://github.com/Developer-Y/cs-video-courses) 🆓

- [Data science courses](https://github.com/DataScienceSpecialization/courses) 🆓

- [Dive into machine learning](https://github.com/hangtwenty/dive-into-machine-learning) 🆓

[![CC4](https://img.shields.io/badge/license-CC4-0a0a0a.svg?style=flat&colorA=0a0a0a)](https://creativecommons.org/licenses/by/4.0/)

[![Lists](https://img.shields.io/badge/-more%20lists-0a0a0a.svg?style=flat&colorA=0a0a0a)](https://github.com/learn-anything/curated-lists)

[![Contribute](https://img.shields.io/badge/-contribute-0a0a0a.svg?style=flat&colorA=0a0a0a)](contributing.md)

[![Twitter](http://bit.ly/latwitt)](https://twitter.com/learnanything_)