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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Hardcover – 11 Jul 2014


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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) + The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
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Product details

  • Hardcover: 426 pages
  • Publisher: Springer; 1st ed. 2013. Corr. 4th printing 2014 edition (11 July 2014)
  • Language: English
  • ISBN-10: 1461471370
  • ISBN-13: 978-1461471370
  • Product Dimensions: 23.9 x 16.3 x 2.5 cm
  • Average Customer Review: 4.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 213,658 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Most Helpful Customer Reviews

3 of 4 people found the following review helpful By Maryam on 31 Oct 2013
Format: Hardcover Verified Purchase
its a good book, and anyone in the field would indeed find it very beneficial, but it doesn't break everything down all the time. you'll find yourself having to study or revise some topics in order to comprehend
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0 of 2 people found the following review helpful By Amazon Customer on 11 Nov 2013
Format: Hardcover Verified Purchase
this book is really helpful to refer about economertics analysis, and I love its hard cover. it is worth to buy it when study R software
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 31 reviews
55 of 57 people found the following review helpful
Excellent Practical Introduction to Learning 24 Oct 2013
By K. Michael Tsiappoutas - Published on Amazon.com
Format: Hardcover Verified Purchase
The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). The authors make no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics."

ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises.

ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better.

A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly.
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UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Amazing opportunity! Enjoy!
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UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.
18 of 19 people found the following review helpful
wonderful but watch the movie 14 Feb 2014
By I Teach Typing - Published on Amazon.com
Format: Hardcover Verified Purchase
This is a wonderful book written by luminaries in the field. While it is not for casual consumption, it is a relatively approachable review of the state of the art for people who do not have the hardcore math needed for The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). This book is the text for the free Winter 2014 MOOC run out of Stanford called StatLearning (sorry Amazon will not allow me to include the website). Search for the class and you can watch Drs. Hastie and Tibshirani teach the material in this book.
4 of 4 people found the following review helpful
cover all of your bases 26 Jan 2014
By Joseph Johnson - Published on Amazon.com
Format: Hardcover Verified Purchase
If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful;

1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones.
2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book.
3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best.
4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced.
5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling.
6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present.

The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.
2 of 2 people found the following review helpful
A solid, well organized primer on Machine Learning in R 10 Jan 2014
By Gary Montry - Published on Amazon.com
Format: Hardcover
This book is probably not for experts (Springer has a more advanced book on Statistical Learning) but it's great for people who intermediate intermediate R users with a reasonable grasp of regression. The examples are easy to follow and the explanations are clear. This book is meant as a guide to IMPLEMENTING machine learning techniques in R. It does not cover the theory or the math behind the methods, nor does it offer proofs.

If you need a practical guide to implementing statistical learning in R, this is an excellent choice. Very understandable and accessible.
1 of 1 people found the following review helpful
Start Here 10 July 2014
By sean christe - Published on Amazon.com
Format: Hardcover Verified Purchase
Everything the other reviews say about this book is completely accurate. I'm currently taking the Coursera Machine Learning course with Andrew Ng - the combination of his lectures and this book have made for a way better learning experience than anything I ever had in school. This book is awesome for somebody that actually needs an introduction to this stuff.
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