An introduction to statistical learning with applications in r solutions

Select Your Cookie Preferences

We use cookies and similar tools that are necessary to enable you to make purchases, to enhance your shopping experiences and to provide our services, as detailed in our Cookie Notice. We also use these cookies to understand how customers use our services (for example, by measuring site visits) so we can make improvements.

If you agree, we’ll also use cookies to complement your shopping experience across the Amazon stores as described in our Cookie Notice. This includes using first- and third-party cookies, which store or access standard device information such as a unique identifier. Third parties use cookies for their purposes of displaying and measuring personalised ads, generating audience insights, and developing and improving products. Click ‘Customise Cookies’ to decline these cookies, make more detailed choices, or learn more. You can change your choices at any time by visiting Cookie Preferences, as described in the Cookie Notice. To learn more about how and for what purposes Amazon uses personal information (such as Amazon Store order history), please visit our Privacy Notice.

Winner of the 2014 Eric Ziegel award from Technometrics.

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. Each chapter includes an R lab. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

The book has been translated into Chinese, Italian, Japanese, Korean, Mongolian, Russian and Vietnamese.

This book is a very nice introduction to statistical learning theory. One of the great aspects of the book is that it is very practical in its approach, focusing much effort into making sure that the reader understands how to actually apply the techniques presented. The book does this by demonstrating their use in the freely available R language. At the end of each chapter are sample R sessions that present the inputs and outputs when running the various techniques discussed in the chapter text on actual data.

This is a great approach because it enables the reader to quickly study and experiment with a great number of machine learning using actual R code and data. It is then an easy exercise to modify the R code to work on different data sets if desired. For the applied statistician this is a great help because it cuts the research time down considerably. I'm not the only one who has a very high view of this book. Readers can see what others have said here.

To make sure I understood this material as well as possible, as I read the book, I worked all the conceptual and applied exercises at the end of each chapter. Linked to this page are the R scripts I wrote for each chapter. It is my hope that students of machine learning and statistics will find this material helpful. In addition to the R scripts I wrote up solutions to these exercises and put them in book form.

Originally these notes and solutions were written in PDF (using the mathematical typesetting language LaTeX). I converted the PDF format to a format I thought more people would find easier to read. You can preview and buy a kindle version of the book here. If you are interested in purchasing the PDF version you can do so for $41.00 (US dollars) (please see the links below).

Introduction to Statistical Learning - Chap10 Solutions

This is the solutions to the exercises of chapter 10 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap9 Solutions

This is the solutions to the exercises of chapter 9 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap8 Solutions

This is the solutions to the exercises of chapter 8 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap7 Solutions

This is the solutions to the exercises of chapter 7 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap6 Solutions

This is the solutions to the exercises of chapter 6 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap5 Solutions

This is the solutions to the exercises of chapter 5 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap4 Solutions

This is the solutions to the exercises of chapter 4 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap3 Solutions

This is the solutions to the exercises of chapter 3 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Introduction to Statistical Learning - Chap2 Solutions

This is the solutions to the exercises of chapter 2 of the excellent book "Introduction to Statistical Learning".

over 7 years ago

Next Word Prediction App Pitch

R presentation for the Data Science Capstone project at Coursera

almost 8 years ago

Data Science Capstone - Milestone Report

Milestone Report for Coursera Data Science Capstone Project

almost 8 years ago

Prediction of Old Faithful geyser eruption duration

Course project for Data Developing Products on Coursera

over 8 years ago

Summary of Severe Weather Events Statistics across the USA (1993-2011)

Peer assessment 2 for the Reproducible Research course on Coursera

over 8 years ago