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I have Deep Learning with Python (Chollet, first edition) and Hands-On Machine Learning (Géron, first edition). Both books are highly recommended.

Introduction to Statistical Learning is also available for free online:

http://faculty.marshall.usc.edu/gareth-james/ISL/

Although I only read a few chapters from that book, I really like it (but I would have preferred a python version of the book).

Personally, if you have to pick three books from the list, ypu can start with these three options.

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You can find a couple of repos (google them) that show the exercises in Python, I had written a post on my own blog some time ago: https://www.franzoni.eu/machine-learning-a-sound-primer/

Good to know, thanks for the link!

I wholeheartedly second "Deep Learning with Python" by François Chollet!

It's an excellent 'zero-to-hero' text for understanding deep neural networks, some common architectures, and the code (and theory) to get them to work.

One thing missing is how to prepare data for deep learning -- but that's just standard ETL you learn elsewhere.

You can check Géron's book to know more about data preparation, specifically the second chapter. This chapter details an end-to-end machine learning project (price prediction). Here, the author describes scikit learn's pipelines for automating preprocessing tasks for your dataset.