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.
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.