Recently finished reading
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists ( link to Amazon)
by Alice Zheng and Amanda Casari.
Great coverage of the topic, that is often omitted in Machine Learning intro books, or books on ML infrastructure.
Picked up tons of programmatic tricks on how to deal with numerical and categorical data. There are good examples in Python.
Several spots in the book are in need of more attention or re-work, imho.
The code printouts were quite long, and were hard to follow on Kindle or iPad.
Also, the appendix section on linear modeling and linear algebra doesn’t seem to belong. And it can be part of the Feature Engineering topic, but in a more intuitive and down to earth way – the coverage seemed a bit more abstract that it could benefit the reader.