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1. #An introduction to statistical learning solutions software
2. #An introduction to statistical learning solutions code

In addition, many popular R packages and data sets will be used.

#An introduction to statistical learning solutions code

All of the code examples for the book are written in R. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. The R statistical environment was chosen for use in this book. This book can be viewed as a set of essential tools we need for a long-term career in the data science field - recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. 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.

#An introduction to statistical learning solutions 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. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.Ĭolor graphics and real-world examples are used to illustrate the methods presented. This book presents some of the most important modeling and prediction techniques, along with relevant applications.