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An Introduction to Statistical Learning: With Applications in R

4.63  ·  Rating Details ·  326 Ratings  ·  27 Reviews
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 techniqu ...more
Hardcover, 426 pages
Published December 14th 2015 by Springer (first published June 24th 2013)
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(showing 1-30 of 1,274)
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grixor
Sep 01, 2016 grixor rated it it was amazing
Excellent book!

The book explains concepts of Statistical Learning from the very beginning. The core ideas such as bias-variance tradeoff are deeply discussed and revisited in many problems. The included R examples are particularly helpful for beginners to learn R. The book also provides a brief, but concise description of functions' parameters for many related R packages.

My professor thinks this book is a "superficial" version of The Elements of Statistical Learning, but I disagree. Yes, it may
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Marco
Mar 26, 2016 Marco rated it it was amazing
A good introduction to the methods of statistical learning, presenting techniques in a clear way and showing some of the practical issues involved in real-world use of regression and classification models. While some math is unavoidable when defining the tools presented in this book, the formulas are kept at a level that might be suitable for those with less mathematical baggage than willingness to understand the concepts, and the R exercises can be very useful to the more practically-minded rea ...more
Josh Davis
I took a Machine Learning class during my last semester. This is the book that was used for the course (we also used Elements of Statistical Learning as the secondary text). I loved it. I thought the explanations were great as well as the exercises. I took the online course offered through Stanford at the same time and got to watch Trevor Hastie & Rob Tibshirani themselves. The videos were hilarious and informative. I'd highly recommend reading the book as well as taking the online course.
Eric
Jan 04, 2015 Eric rated it it was amazing
Clear, intuitive exposition of a subset of methods in statistical learning. Great illustrations and plenty of R code. My only complaint is that the R code is quite ugly looking, which is no surprise since it was written by statisticians, but the authors should be forgiven for this minor infraction. Overall I highly recommend this book.
Shalini
Jan 03, 2016 Shalini rated it it was amazing
The book starts with a good introduction to basic classifiers, their differences, why we need each one of them or why we don't. It also mentions evaluators for each kind of classifier and explains how they are relevant in the beginning chapters. This is extremely helpful since it provides a holistic view of the flow which will be explained in further chapters. Much better intro to machine learning compared to other books. Loads of problems to work on which makes sure the understanding has seeped ...more
Ji
Jan 10, 2016 Ji rated it it was amazing
Shelves: technical
A great book to get started with basic theory behind statistical learning methods. I have to admit that I went through the book in a rush and barely spent enough time to cover the whole book. It's going to be worthy of a revisit in the future per I jumped into quick questions in some theoretical foundations. Good for anybody who wants to pick up machine learning theories using R, with limited or little prior knowledge in both fields.
Rajesh
Feb 08, 2016 Rajesh rated it it was amazing
Introduction to Statistical Learning - commonly known as ISL - is a great book. It is now available for free on the authors' website - and is one of the best introductions to machine learning available. There's a lot of hype and interest in this area of mathematical modeling these days, because of the data science and big data revolutions that are in progress. If you have a propensity to use R, rather than Python, or want to get to know some of the fundamentals of the discipline, this is a good ...more
Jerzy
Aug 09, 2016 Jerzy marked it as to-read
Shelves: statistics
Skimmed just through Ch 3 (linear regression) so far. Hoped it'd be something I can recommend to a total novice, but it isn't. That's fine---it's just for a higher-level audience than I was hoping.

Based on my experience TA'ing statistical novices, I suspect the linear regression stuff is already too dense and rushed to help them really understand what's going on & why. They'll need a little more time on each aspect, a few more examples, a little deeper sense of why we do these things.
On the
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Rodrigo Rivera
Mar 30, 2014 Rodrigo Rivera rated it it was amazing
Wie schon hier erwähnt, ist An Introduction to Statistical Learning (ISL) eine ausgezeichnete Einführung ins Machine Learning. Man kann es als den kleinen Bruder von "The Elements of Statistical Lernen" (ESL) sehen. Es werden alle relevanten Themen vom Statistical Learning/Machine Learning (classification, clustering, supervised, unsupervised, usw.) in wenigen Seiten behandelt. Denn ISL ist extrem gut erklärt und benutzt eine einfache Sprache. Wenn man noch zusätzlich das Stanford MOOC "Statisti ...more
Luke Duncan
Sep 16, 2016 Luke Duncan rated it it was amazing
I loved this book. If you're a practitioner looking to pick up a skill set in Machine Learning this is a good place to start. If you're looking to deep dive on every proof necessary to back up the assertions in the book, prefer the authors original book "Elements of Statistical Learning." I wrote a detailed review at https://www.linkedin.com/pulse/introd...
Skanda Vasudevan
Nov 11, 2014 Skanda Vasudevan rated it really liked it
Shelves: machine-learning
A very nice book on statistical techniques. Mostly the book deals with linear models and in particular linear function approximation is explained in more detail. Nevertheless, this is a very good start to get a feel for in depth statistical analysis. Additionally, a course is offered on stanford which is accessible and it is based on the text and taught by the authors themselves.
Alex
Apr 17, 2016 Alex rated it really liked it
A surprisingly readable introduction to statistical learning techniques. Interesting to read about classical regression methods as special cases of machine learning techniques. Generally, the math-lite treatment served the book well, except the section on SVM, which needs some more mathematical meat to actually make sense.
Branca
Aug 15, 2016 Branca rated it it was amazing
Shelves: acad-micos
Em prol do meu estágio de Verão (Fid) fui convidada a ler este livro para então poder iniciar o meu modelo. Óptima linguagem. Super acessível para quem (apenas) quer saber de uma abordagem prática das novas técnicas de Machine/Statistical Learning.
Para tópicos que ficaram mal desenvolvios teoricamente, recomendo "The Elements of Statistical Learning": https://www.goodreads.com/book/show/1... . Este não o li por completo. Apenas o consultei para aprofundar certos tópicos - boosting, bagging, ran
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Barbara
Oct 02, 2014 Barbara rated it it was amazing
Shelves: favorites
It isn't often that you pick up a statistical text book to browse and find yourself so absorbed that you keep reading for seven hours straight. Lots of "ah hah!" moments for me. Everything is clearly laid out: how, when, and why you might use each of the methods covered, how to test your models, and the basic logic (but not the complicated maths) behind how they work. To top it off, clear worked examples are given in R, along with problem sets to get your brain engaged.

Possibly probability dist
...more
Alan
Aug 27, 2016 Alan rated it it was amazing
A must for machine learning learners, good from introductory to intermediate level
Mạnh Tài
Nov 05, 2014 Mạnh Tài rated it it was amazing
One of the bests in the sense of 'introduction' about statistical learning.
Ali Baum
Jan 28, 2014 Ali Baum rated it really liked it
Great review of (or intro to) stats, supervised & unsupervised learning, & R. Well-written & well-annotated sample code.
Piyush Maheshwari
Oct 07, 2015 Piyush Maheshwari rated it really liked it
Great introductory text with clear, intuitive and easy to follow explanations. However being an introductory book, it does lack at mathematical rigor at places.
Truc-Vien Nguyen
Mar 11, 2015 Truc-Vien Nguyen rated it it was amazing
Shelves: science
Quite solid, clear and practical for statistical learning, but also easy to understand. I got a kindle edition and used it as reference book. It covers main topics in statistical learning methods, from statistics for complex datasets, yet not require readers to have a strong mathematical background.
Hans Tunggajaya
Jun 16, 2014 Hans Tunggajaya rated it liked it
The book is suitable for those interested to machine learning but has not strong background in mathematics. Most of the explanations are intuitive rather than rigorous. For those who have strong background in mathematics, this book can serves as an introduction, as the title suggests.
Akshay Chougule
Jul 28, 2016 Akshay Chougule rated it it was amazing
Shelves: career
Good book to start with if you don't have a formal education in statistics but it's part of your daily job.

It is a watered-down version of "Elements of statistical learning" which I couldn't understand in my first attempt.

Great book for technical interview preparation.
Samuel Brown
Nov 04, 2013 Samuel Brown rated it it was amazing
Great intro to bring you up to speed (or back up to speed, as the case may be) before launching into the more technical _Elements of Statistical Learning_ from these authors. Well written, useful, well annotated sample code.
João
Mar 16, 2015 João rated it it was amazing
Best thing out there to understand the fundamentals of machine/statistical learning with explicit examples of how to apply the theoretical concepts in the R programming environment.
Nancy
Jun 30, 2015 Nancy rated it really liked it  ·  review of another edition
Shelves: reference
- Read Chapters 1-6, tons of highlights and great wisdom to review again from time to time again
- TODO: read 7-9 for regression info
- Read Chapter 10 for the PCA segment
Shuyi Ma
May 15, 2014 Shuyi Ma rated it really liked it
clear explanations of concepts, help to conceptualize the methods and how to interpret.
Santiago Ortiz
Dec 17, 2015 Santiago Ortiz rated it it was amazing
Shelves: work, math, data, to-repeat
Comprehensive and clear, excellent book.
Sonja Hennessy
Sonja Hennessy marked it as to-read
Sep 24, 2016
Pushp
Pushp marked it as to-read
Sep 22, 2016
Xing
Xing rated it it was amazing
Sep 22, 2016
Junior
Junior is currently reading it
Sep 22, 2016
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