🔘 Book page: dafriedman97.github.io/mlbook/content/table_of_contents.html


“This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox so they have the right tool for a variety of tasks. Each chapter in this book corresponds to a single machine learning method or group of methods. In other words, each chapter focuses on a single tool within the ML toolbox […]. This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish”. (Source: Derivation in concept and code, dafriedman97.github.io/mlbook/content/introduction.html)


Danny Friedman. Stats Major at Harvard and Data Scientist in Training. (Source: https://towardsdatascience.com/@dafrdman).