[bradreaves2]

This is off the beaten path, but consider Abu-Mostafa et al.'s "Learning from Data". https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/...

I adore PRML, but the scope and depth is overwhelming. LfD encapsulates a number of really core principles in a simple text. The companion course is outstanding and available on EdX.

The tradeoff is that LfD doesn't cover a lot of breath in terms of looking at specific algorithms, but your other texts will do a better job there.

My second recommendation is to read the documentation for Scikit.Learn. It's amazingly instructive and a practical guide to doing ML in practice.

[vowelless]

I strongly second this. Abu Mostafa has videos and homework for this course too. This course was the one that made a LOT of fundamental things “click”, like, why does learning even work and what are some broad expectations about what we can and cannot learn.

[PartiallyTyped]

LfD is a great book to get people to think about complexity classes and model families. We used that in my grad course and I can recommend it.