awesome-full-stack-machine-learning-courses
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
Browse a collection of free machine learning courses from top universities, organized by topic and specialization.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeCurated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
awesome-full-stack-machine-learning-courses has 530 stars on GitHub. It has been forked 109 times. awesome-full-stack-machine-learning-courses is written mainly in JavaScript. It has been in active development since 2019. awesome-full-stack-machine-learning-courses is available under the CC0-1.0 license. Its main topics are berkeley, berkeley-ai, berkeley-reinforcement-learning, caltech.
What is awesome-full-stack-machine-learning-courses?
Curated list of publicly accessible machine learning engineering courses from CalTech, Columbia, Berkeley, MIT, and Stanford.
Is awesome-full-stack-machine-learning-courses open source?
awesome-full-stack-machine-learning-courses is an open-source project. It is released under the CC0-1.0 license.
Is awesome-full-stack-machine-learning-courses free?
Yes. awesome-full-stack-machine-learning-courses is free and open source — you can use, modify and self-host it.
What license does awesome-full-stack-machine-learning-courses use?
awesome-full-stack-machine-learning-courses is available under the CC0-1.0 license.
What language is awesome-full-stack-machine-learning-courses written in?
awesome-full-stack-machine-learning-courses is written mainly in JavaScript.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/leehanchung-awesome-full-stack-machine-learning-courses.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.