If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you.  Those two algorithms are commonly used in a variety of applications including big data analysis for industry and data analysis competitions like you would find on Kaggle.

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The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

We assume the reader has some prior exposure to (supervised) ML and other relevant mathematical topics (e.g., probability, statistics, linear algebra, optimization). This background material is covered in the prequel to this book, [Probabilistic Machine Learning: An introduction], although the current book is self-contained, and does not require that you read [Probabilistic Machine Learning: An introduction] first.