Learning From Data
Comments:
From my other comment elsewhere.
If anyone wants to delve into machine learning, one of the superb resources I have found is, Stanfords "Probability for computer scientists"(https://chrispiech.github.io/probabilityForComputerScientist...).
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 [1]. 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 [2].
There is also book for excellent caltech course[3].
[1] https://chrispiech.github.io/probabilityForComputerScientist...
[2] https://work.caltech.edu/telecourse
[3] https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...
That's because they posted them somewhere else (easy mistake to make.. HN doesn't show you the full link in a comment, so copy/paste just copies the ellipsis)
https://chrispiech.github.io/probabilityForComputerScientist...
https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...