It is the latter portion of the update that I’ve been waiting for as it directly applies to my work in data science. The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples. My familiarity with it comes from the Stanford University graduate program in computer science and mathematical statistics (in dated nomenclature, “data mining”). This book has been front and center on my research bookshelf for years. An Introduction to Statistical Learning with Application in R by James, Witten, Hastie, and Tibshirani is a contemporary re-work of the classic machine learning text Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. It is a book for which I’ve been waiting a long time. Anyone who wants to intelligently analyze complex data should own this book.I’m excited to be writing this book review. The authors give precise, practical explanations of what methods are available, and when to use them, including explicit R code. ISL makes modern methods accessible to a wide audience without requiring a background in Statistics or Computer Science.
Inspired by "The Elements of Statistical Learning'' (Hastie, Tibshirani and Friedman), this book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods.
"An Introduction to Statistical Learning (ISL)" by James, Witten, Hastie and Tibshirani is the "how to'' manual for statistical learning. Even if you don’t want to become a data analyst-which happens to be one of the fastest-growing jobs out there, just so you know-these books are invaluable guides to help explain what’s going on.” (Pocket, February 23, 2018) “Data and statistics are an increasingly important part of modern life, and nearly everyone would be better off with a deeper understanding of the tools that help explain our world. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Her research focuses largely on statistical machine learning in the high-dimensional setting, with an emphasis on unsupervised learning. The conceptual framework for this book grew out of his MBA elective courses in this area.ĭaniela Witten is an associate professor of statistics and biostatistics at the University of Washington. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. Gareth James is a professor of data sciences and operations at the University of Southern California. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Color graphics and real-world examples are used to illustrate the methods presented. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. This book presents some of the most important modeling and prediction techniques, along with relevant applications. An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.