There are three types of machine learning books — books written for people who want to become machine learning engineers, books written for people who want to become machine learning researchers, and books written for business executives. Reading a book written for researchers or executives can be a frustrating experience if you are a software engineer, social scientist, or mechanical engineer who wants to learn machine learning and get an ML job in the industry.
If you are a coder who wants to become an ML engineer, you have got to learn machine learning concepts, but you want to learn them in a practical way. You need a book that leads with intuition and shows you implementations with code. It has to do this without getting sidetracked into ML theory, getting mired in statistical concepts, or being so superficial that you don’t understand why the code works. Aurélien gracefully threads this needle — that’s what makes his book so good.
This is a very clearly written book. The author uses a simple framework (scikit-learn) to explain the basics, and then moves to TensorFlow for more realistic examples. Throughout, the book is immensely pragmatic. I strongly recommend this as your first ML book.