This book is about how to make features for machine learning models and implement them into models. The book goes into natural language text, tabular data, and image data. It contains discussions about how to implement good engineering practices in feature engineering.
This book is about methods and ways to understand AI and data modeling and how to utilize the different ways of interpreting machine learning models. It gives the basis and then dives into the different types of models and methods you can use. It separates the models into specific to models or model families, and model agnostic.
This book is about the practical implementation of machine learning models. It goes through why machine learning should be used, how to implement it, and how to execute in all phasers of the machine learning life-cycle.