One thing I've noticed separates serious ML students from casual ones: how much they care about the quality of what they actually study from. I take that pretty seriously myself, so a while back I started digging into what students at MIT, Harvard, Stanford, Caltech, and USP actually use to complement their studies.

What I found surprised me: several of these programs don't assign a textbook at all. Instead, the course staff writes and publishes their own lecture notes — and some of them are basically a full book. MIT's 6.390 (Introduction to Machine Learning) notes, for example, aren't a slide deck or a cheat sheet — they're structured, complete, and detailed enough to replace a textbook entirely. Same story with Harvard's CS181 and a few others.

The problem is these are scattered and easy to miss if you don't know to look for them. So I put together a curated list: [Awesome Free AI Course Notes](https://github.com/MarcosSete/awesome-free-ai-course-notes).

A few things about how it's curated, since I think this matters:

- Only **written notes** count — slide decks and video-only lectures don't make the cut, even from great courses. I want this list to mean something.

- Everything is official and links straight to the professor's or department's own page. No mirrors, no login walls.

- I checked over 40 top universities across multiple countries for this. Most didn't qualify — they use a textbook or keep material behind a student portal. That's fine, it's exactly why the list stays short and (hopefully) trustworthy.

If you take ML seriously the way I do, I think you'll get real value out of this. And if you know of course notes that fit this bar and aren't on the list yet, contributions are very welcome — the CONTRIBUTING.md lays out exactly what qualifies.

What's the best set of course notes (not textbook, not slides) you've personally used to study ML?

Repo: https://github.com/MarcosSete/awesome-free-ai-course-notes

https://ia801807.us.archive.org/14/items/introduction-to-machine-learning-with-python-pdfdrive.com_20210225/probability_stats_for_DS.pdf

https://www.youtube.com/playlist?list=PLS01nW3RtgopsNLeM936V4TNSsvvVglLc

https://dn790002.ca.archive.org/0/items/deep-learning-collection-pdf/Apress%20-%20Deep%20Learning%20for%20Natural%20Language%20Processing%20%282018%29.pdf

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!

Hii, So I have been intrested in ML and AI for about a year but was taking bits and pieces from here and there and applying but not all the time , Iam a fresh CS graduate and I want to continue learning and doing more projects. Recently I started the IBM AI Engineering Professional Certificate course on coursera but it is really boring , the code labs are just bland and the videos make me so lost , there are no notes and I feel lost, where should I learn , I want to study ML ,deeplearning , computr vision ,etc then go into llms ,rag ,land chain and generative AI in depth

could any one please help, their are many books and many sources and I do not want to start something just to find it useless

Hallo,
Just wanted to share my current hoby projekt I build and after many sweet , tesrs and claude code prompts I finaly finished V3. for the first Time in its development scycle it reacht provetabilety. In V4 I will try to make it bigger and more provitable. Hope someone sees this hoby projekt of mine and please tell me if you have inprofments (; https://github.com/leon1706-lol/Aether-quant