Data Science and Machine Learning

'The way the Python code was written follows the algorithm closely. This is very useful for readers who wish to understand the rationale and flow of the background knowledge. In each chapter, the authors recommend further readings for those who plan to learn advanced topics. Another useful part is that the Python implementation of different statistical learning algorithms is discussed throughout the book. At the end of each chapter, extensive exercises are designed. These exercises can help readers understand the content better. This book would be a good reference for readers who are already experienced with statistical analysis and are looking for theoretical background knowledge of the algorithms.'

-Yin-Ju Lai and Chuhsing Kate Hsiao, Biometrics, vol 77, issue 4, 2021

"The first impression when handling and opening this book at a random page is superb. A big format (A4) and heavy weight, because the paper quality is high, along with a spectacular style and large font, much colour and many plots, and blocks of python code enhanced in colour boxes. This makes the book attractive and easy to study...The book is a very well-designed data science course, with mathematical rigor in mind. Key concepts are highlighted in red in the margins, often with links to other parts of the book...This book will be excellent for those that want to build a strong mathematical foundation for their knowledge on the main machine learning techniques, and at the same time get python recipes on how to perform the analyses for worked examples."
- Victor Moreno, ISCB News, December 2020

"This textbook is a well-rounded, rigorous, and informative work presenting the mathematics behind modern machine learning techniques. It hits all the right notes: the choice of topics is up-to-date and perfect for a course on data science for mathematics students at the advanced undergraduate or early graduate level. This book fills a sorely-needed gap in the existing literature by not sacrificing depth for breadth, presenting proofs of major theorems and subsequent derivations, as well as providing a copious amount of Python code. I only wish a book like this had been around when I first began my journey!"
- Nicholas Hoell, University of Toronto

"This is a well-written book that provides a deeper dive into data-scientific methods than many introductory texts. The writing is clear, and the text logically builds up regularization, classification, and decision trees. Compared to its probable competitors, it carves out a unique niche."
- Adam Loy, Carleton College

"The data is the fuel of the new industry of the future, and this new science based in statistic and mathematical modeling have a deep background that must be learnt to understand the gist this new technology and theirs applications. This year I have gotten a certificate in Machine Learning in the MIT and of course I studied from several excellent books, but just one cover all the fundamental knowledge in a clear, rigorous and elegant way. Even with phyton programming to test the algorithms and stay in touch in a real way with the mathematical technics required to learn in a professional way. The book have other important advantage, the format is big, clean and full of colour, more when one must understand an specific notation in a rigorous way. A great book really thought in the students who want to progress in this subject. A splendid job of professor Dirk P. Kroese and his colleagues."
- Marcelo Cortes

"I'm very early on in the text, but the text is impressive in the breadth and depth of its coverage, along with its attention to the mathematical theory. The exercises are challenging, which makes the text a little tricky for self-study -- at least to the extent your self-study is enhanced by knowing if you got the problems right or wrong. If I ever make it through the whole text, and change my opinion, I'll come back and edit this review; but I think just getting through Chapter 2 will be a semester's worth of knowledge."
- Richard Rivero