An Introduction To Statistical Learning: With Applications In R
ISBN: 1071614177
EAN13: 9781071614174
Language: English
Pages: 426
Weight: 1.737243 lbs.
Format: Hardcover
Publisher:

An Introduction To Statistical Learning: With Applications In R

by
$63.05
List Price: $117.38
Save: $54.33 (46%)
Select Format
`
Select Format Format: Hardcover Select Conditions Condition: Good

Selected

Format: Hardcover

Condition: Good

$63.05
List Price: $117.38
Save: $54.33 (46%)
Quantity
Almost Gone!
Only 2 at this price.

Select Conditions
  • Good $63.05 An Introduction To Statistical Learning: With Applications In R
  • Very Good $68.52 An Introduction To Statistical Learning: With Applications In R
  • New $117.38 An Introduction To Statistical Learning: With Applications In R
Book Overview

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. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. 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.

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. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at Read More chevron_right

Frequently Asked Questions About An Introduction To Statistical Learning: With Applications In R

Book Reviews (4)

4
  |   4  reviews
Did you read An Introduction To Statistical Learning: With Applications In R? Please provide your feedback and rating to help other readers.
Write Review
Captcha
5
   Excellent book
I used this book recently, along with a few others in a Bachelor's Level ML course. In terms of achieving a good balance, it was IMO the best. containing enough detail to help the grasp theory, but not so much that it becomes a long slog to get through. I have used the e-book in class and liked it enough to buy a hardcover. Unfortunately the type of print is quite small. The total dimensions is smaller than listed on Amazon. That was maybe how big the 1st edition was?
 
4
   Great book, poor print job
They cover most of the key relevant topics in a readily accessible way. It may disappoint those who want a more rigorously mathematical treatment in depth, but is really not aimed at them. The only thing to regret about this book is the print job. The pages are good, but the hardcovers are super easy to read and the binding is tight and brittle. It also appears as Springer scrimped on the page sizes because the margin text is hard to read. For anyone who wants to escape the paper job, the book is available for free as a. pdf from the authors.
 
1
   Book uses too small font size
I understand that it is important to reduce the price of a book. I was so enthusiast about reading this book, but the font size used for the text is quite small.
 
5
   Great book to broaden understanding
This book is an amazing resource to get your understanding in line with many different methods. One of the greatest tools of a data scientist and statistician in general is knowledge of the best technique or the best tool for a task. Many solutions in data science are now far too heavily geared toward one size fits all and this book helps understanding why it really does matter how to read your results and how to use the method to solve it.
 
1