Comment by rottc0dd

From my other comment elsewhere. These resources helped me understand the topics better.

If anyone wants to understand fundamentals of machine learning, one of the superb resources I have found is, Stanford's "Probability for computer scientists"[1].

It goes into theoretical underpinnings of probability theory and ML, IMO better than any other course I have seen. But, this is a primarily a probability course that discusses the fundamentals of machine learning. (Yeah, Andrew Ng is legendary, but his course demands some mathematical familiarity with linear algebra topics)

There is a course reader for CS109 [2]. You can download pdf version of this. Caltech's learning from data was really good too, if someone is looking for theoretical understanding of ML topics [3].

There is also book for excellent caltech course[4].

Also, neural networks zero to hero is for understanding how neural networks are built from ground up [5].

[1] https://www.youtube.com/watch?v=2MuDZIAzBMY&list=PLoROMvodv4...

[2] https://chrispiech.github.io/probabilityForComputerScientist...

[3] https://work.caltech.edu/telecourse

[4] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

[5] https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxb...