Pdf an introduction to statistical learning

#Pdf an introduction to statistical learning how to
#Pdf an introduction to statistical learning software

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.

#Pdf an introduction to statistical learning how to

Inspired by 'The Elements of Statistical Learning'' Hastie, Tibshirani and Friedmanthis book provides clear and intuitive guidance on how to implement cutting edge statistical and machine learning methods. What REALLY is Data Science? Told by a Data Scientist

Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. 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. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. 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 editiona popular reference book for statistics and machine learning researchers.

#Pdf an introduction to statistical learning software

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. A word in response to the corona virus crisis: Your print orders will be fulfilled, even in these challenging times.