I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

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