This seems like a great resource for understanding how machine learning algorithms actually work. Anyone reading the book to help them build more accurate model

This seems like a great resource for understanding how machine learning algorithms actually work. Anyone reading the book to help them build more accurate models, might also be interested in supplementing it with more research into the importance of feature engineering before training machine learning models. While feature engineering hasn't been a rigorously studied within the academic literature, this book does have a section on feature generation, which gives some practical tips once your data is in feature matrix form. For those interested in even more, I work on a python library for automated feature engineering called, Featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/). It can help when your raw data is still too granular for modeling or comprised of multiple tables. We have several demos you can run yourself to apply it to real datasets here: https://www.featuretools.com/demos https://www.featuretools.com/demos.