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**An Introduction to Statistical Learning with Applications in R **

In **Machine Learning**, This book is an introduction to statistical learning methods. It is aimed at undergraduate, master’s and postgraduate students. non-mathematical science students. The book also includes various R labs with detailed explanations on how to implement the various methods in a real world environment and will be a valuable resource for a practicing data scientist.

**An introduction to statistical learning methods, this book contains several R labs with detailed explanations on how to implement different methods in a real-world environment.**

**Book Description:**

Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential set of tools to make sense of the vast and complex datasets that have emerged in fields ranging from biology to finance, marketing and astrophysics in the past. twenty years. This book introduces some of the most important modeling and forecasting techniques, along with their applications. Topics include linear regression, classification, resampling methods, reduction approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple tests, and more. Full-color graphics and real-world examples are used to illustrate the methods presented. As the goal of this textbook is to facilitate the use of these statistical learning techniques by professionals from science, industry and other fields, each chapter contains a tutorial on implementing the analyzes and methods. presented in R., an extremely popular open source statistical software platform. . Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani, and Friedman, 2009 Second Edition), a popular reference book for statistics and machine learning researchers. Introduction to statistical learning covers many of the same topics, but at a level accessible to a much wider audience. This book is aimed at statisticians and non-statisticians who wish to use state-of-the-art statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

This second edition features new chapters on deep learning, survival analysis and multiple testing, as well as extended treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. The R code has been updated to ensure compatibility.

**An Introduction to Statistical Learning – with Applications in R**

Author(s): Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Series: Springer Texts in Statistics

Publisher: Springer Science+Business Media, Year: 2021

ISBN: 9781071614174,9781071614181

**an introduction to statistical learning with applications in r second edition pdf**

**[PDF] An Introduction to Statistical Learning with Applications in R Table Of Contents**

Introduction

Statistical learning

Linear regression

Classification

Resampling methods

Linear model selection and regularization

Moving beyond linearity

Tree-based methods

Support vector machines

Unsupervised learning

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