"This book is a valuable resource for a diverse audience, including students, researchers, and professionals in the fields of statistics, data science, and machine learning. Its ability to synthesize theoretical principles with practical applications sets it apart from many other texts in the domain. The authors strike a fine balance between addressing foundational concepts, such as bias-variance tradeoffs and cross-validation, and delving into advanced topics like support vector machines and simultaneous learning. By integrating these elements, the book equips readers with a robust toolkit for tackling real-world challenges. Particularly commendable is its emphasis on practical examples, such as medical case studies, which not only
contextualize the theoretical discussions but also demonstrate their relevance across disciplines. For professionals, the book offers insights into optimizing models and applying techniques to complex datasets, bridging the often-intimidating gap between theory and practice." - Fransiskus Serfian Jogo,
Techonometrics, February 2025
"In a nutshell, this is an essential textbook that brings together statistical learning and machine learning in a cohesive and practical framework. The book is meticulously designed for students, data analysts, and professionals who seek a clear and comprehensive understanding of the essential tools and techniques used in data analysis today. One of its main strengths lies in its ability to integrate theoretical concepts from statistics with the computational practices of machine learning, bridging the gap between abstract theory and practical application.
The authors of this book are highly experienced in the field of data science, with an academic background in both statistical theory and computational methods. This combination makes their writing both accessible and authoritative, striking a balance that is often hard to achieve in texts that deal with complex topics. The book covers a broad spectrum of material, beginning with foundational topics like regression analysis and progressing to more sophisticated methods such as support vector machines, decision trees, unsupervised learning, and neural networks. This thorough coverage makes the book a one-stop resource for anyone seeking to master the fundamentals of statistical prediction and machine learning." - Michal Pesta, Charles University, Journal of the American Statistical Association, April 21, 2025
