This book is intended to integrate two data science modeling techniques: Mathematical modeling and Networking modeling. This book approaches the first technique with neural networks and the second with statistical methods. Neural networks begins with a view of parallel relations and statistical methods for data analysis. The chapters contain some mathematical formulas, but you should be able to understand the ideas behind the methods even without the formulas. This book is not for people trying to learn machine learning from scratch. If you are new to machine learning, there are a lot of books and other resources to Learn the basics. New methods for the interpretation of machine learning models are published rapidly. To keep up with everything that is published would be madness and simply impossible. That is why you will not find the most novel and fancy methods in this book, but established methods and basic concepts of machine learning interpret ability. These basics prepare you for making machine learning models interpretable. Internalizing the basic concepts also empowers you to better understand and evaluate any new paper on interoperability.
