awesome-full-stack-machine-learning-courses

Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.

computer-science

View on GitHub

517 Stars

109 Forks

517 Watchers

JavaScript Language

cc0-1.0 License

100 SrcLog Score

Cost to Build

$66.7K

Market Value

$284.0K

How is this calculated?

Growth over time

8 data points · 2021-08-01 → 2026-04-01

Stars Forks Watchers

💬

How do you feel about this project?

Ask AI about awesome-full-stack-machine-learning-courses

Question copied to clipboard

What is the leehanchung/awesome-full-stack-machine-learning-courses GitHub project? Description: "Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.". Written in JavaScript. Explain what it does, its main use cases, key features, and who would benefit from using it.

Question is copied to clipboard — paste it after the AI opens.

How to clone awesome-full-stack-machine-learning-courses

Clone via HTTPS

git clone https://github.com/leehanchung/awesome-full-stack-machine-learning-courses.git

Clone via SSH

[email protected]:leehanchung/awesome-full-stack-machine-learning-courses.git

Download ZIP

Download master.zip

Found an issue?

Report bugs or request features on the awesome-full-stack-machine-learning-courses issue tracker:

Open GitHub Issues

Similar to awesome-full-stack-machine-learning-courses

every-programmer-should-know computer-science papers-we-love javascript-algorithms awesome-courses cs-video-courses Design-Patterns-In-Swift CS-Interview-Knowledge-Map Mind-Expanding-Books translations p1xt-guides AlgoWiki algorithms Crash-Course-Computer-Science-Chinese computer-science-in-javascript pretty-algorithms OOD-Principles-In-Swift influential-cs-books selfie papers-I-read CSrankings reading notes PyCNN computer-science notes-to-cs-freshmen-from-the-future Axelrod judge-server All-About-Programming robotics-coursework