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I really recommend Murphy’s “Machine Learning: a probabilistic perspective”. Murphy’s lays the groundwork for understanding how the algorithms work, why and how they could be adapted to the problem you’re dealing with. It takes you from complete beginner (with a reasonable math level) to one step above `import sklearn as sk`.

The other books I read make the field look like a bunch of heuristics that just happen to work.

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Kevin Murphy's book is already the equivalent of the ML bible in ML grad classes. I am very surprised to not see it at the top.

I absolutely love this book. The breath of topics is unparalleled and my STEM undergrad level math knowledge was perfectly sufficient understanding the math.

Murphy is a nice book, but I’ve always felt like it was more of a survey text and not one made for diving deep into a given subject. For instance, if you want to go from theory to writing code, Murphy isn’t necessarily the best book for that.

I think it was intended to show you the landscape, give you enough tools and background knowledge so you can go and explore the literature by yourself. Years after it’s publication it still does a really good job at it.

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