Only if the general rules of thumbs are fulfilled then will I engage to further. The general problem solving process goes as follows for me:

1. Identify clearly the problems.
2. Translate the problems into a more concrete statement.
3. Gather data
4. Perform exploratory data analysis
5. Determine the correct machine learning solution to use
6. Build a model.
7. Train the model.
8. Test the model.

Throughout the chapters in this book, the pattern above will be followed. The exploratory data analysis sections will be only done for the first few chapters. It's implicit that those would have been done in the later chapters. 

I have attempted to be clear in the section headings on what exactly are we trying to solve, but writing is a difficult task, so I may miss some.