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I'm a big fan of ISL - one of the best intro machine-learning oriented textbooks out there IMO. If you're looking for book that still offers a broad survey while going a bit deeper into the math, I recommend Elements of Statistical Learning as well (they share 2 authors):

https://web.stanford.edu/~hastie/Papers/ESLII.pdf

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ESL is really really good. Absolutely cannot be recommended enough. I’ve worked through at few times and it’s a great book to have on the shelf.

ISl is good but as it being an introduction book — the lack of higher order math wasn’t my favorite.

Recommendation for ESL seconded. One of the best ML books in terms of writing, development of intuition, breadth of topics (ofc doesn't cover everything, esp deep learning).

What I love about the book is how the topics are "connected" so to speak. The narrative within a theme is typically "let's look at problem P, here's technique Q to solve P, but if you thought about P slightly differently you would see something like technique R would also work, so let's talk about that now".

The level of math might be tough for a beginner though.

I can also recommend https://github.com/maitbayev/the-elements-of-statistical-lea... as a supplementary resource that reproduces graphics and implements algorithms from scratch from the book.

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