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An Introduction to Statistical Learning: With Applications in R

4.61  ·  Rating details ·  1,395 ratings  ·  115 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 September 1st 2017 by Springer (first published June 24th 2013)
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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. ...more
afloatingpoint
Jan 30, 2016 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
...more
Eric
Dec 04, 2014 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.
Gavin
Jul 07, 2019 rated it really liked it
Shelves: maths, technical-10x, stats, ml
Really good, heavy on intuition building, folk ML, and stuff which you'll actually use. I've brushed up against all of it before (: I've called all of it from the safe distance of a nice Python library before), but it took a second pass and doing all the exercises to click. To actually learn (grok) something, you need

1. To do it, not just read about it
2. To read it several times
3. To feel challenged but not overwhelmed by it

And 2&3 conflict.

(Most books don't have a natural do-operator. How do yo
...more
Lord_Humungus
Mar 18, 2018 rated it really liked it
Recommends it for: beginners in statistics after their first course
Recommended to Lord_Humungus by: myself
A very good book of statistics that you can read after your Statistics 101 course, centered on machine learning. Very clear prose, very consistent notation, and in general everything that one asks from a good statistics book. I've read 95% of it and it's very good if you don`t know much. I found the exercises quite difficult, though. I have no knowledge of algebra or calculus, so I just could't do some of them. And many things I had to believe by faith. I'm ok with faith, but ocassionally the au ...more
Shalini
Jan 03, 2016 rated it it was amazing
Shelves: non-fiction
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
Marco
Mar 24, 2016 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
Vysloczil
Nov 09, 2016 rated it it was amazing
Shelves: stat-model
Probably the most accessible machine/statistical learning textbook out there. Even understandable for people without rigorous training in statistics or mathematics. Very much based on intuition.

Pay attention to the videos by the authors that follow the chapters of the book (made for a Stanford MOOC but freely accessible on yt: https://www.r-bloggers.com/in-depth-i...).
...more
Stephen Lung
Apr 08, 2018 rated it it was amazing
Amazing book! A great intro to ML and statistical learning with some solid, clear and practical examples. Some of the concepts introduced appear so simple to the human mind, but getting the machine to learn these concepts is a whole different science. This book made me appreciate the wonders of ML. It also reinforced the notion that vast industries will be revolutionized, it is just a matter of time. In this book alone, I learned about the different techniques in supervised learning and unsuperv ...more
Joaquin Menendez
Excellent book for anybody that wants to start adventuring in the marvelous world of data science
Ji
Oct 14, 2014 rated it liked it
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.
A Mig
Mar 17, 2019 rated it it was amazing
Shelves: science-tech
An excellent introduction to statistical learning presenting the main algorithms for both regression and classification (linear regression, logistic regression, lasso, LDA, KNN, tree bagging and boosting, SVM, etc), as well as the important statistical tests (R^2, p-value, ROC, CV, concept of bias-variance tradeoff, etc...). Things are kept very simple with light-weight mathematics. The accompanying R labs help the reader consolidate his knowledge and get his hands dirty on real datasets. The ex ...more
Brian Powell
Apr 30, 2020 rated it really liked it
Shelves: machine-learning
Clear and gentle introduction to non-neural net-based machine learning. Suitable for undergrads, it covers a useful collection of topics that aren't always given emphasis in introductory texts, like resampling methods and model selection. Very clear, very non-pretentious discussion of support vector machines. This text is the smooth chaser to its bristly, discourteous cousin "The Elements of Statistical Learning", a text that should only be consulted much later in life and even then only under d ...more
Metin Ozturk Ozturk
Oct 15, 2018 rated it it was amazing
One of the best introductions to Machine Learning.
Kelsey Edwards
Jun 04, 2020 rated it really liked it
I’m sure I will continue to review the content in this book time and time again as I continue practicing modeling!
marc
Feb 03, 2019 rated it it was amazing
One of the finest intro ML books of our times.
Anna-maria Rebel
Jan 12, 2020 rated it it was amazing
Amazing overview of the algebra behind machine learning methods applied to statistical problems!!!!
Gayathri
Mar 18, 2020 rated it it was amazing
An amazing book to start with the fundamentals of data science.
Marcus
Jul 19, 2018 rated it really liked it
Authorative but very equation heavy. I read three chapters then stopped as I had enough info from those to expand my knowledge.
Jerzy
Feb 15, 2014 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 othe
...more
Milcolumbus Mowlington
Jan 17, 2019 rated it it was amazing
Shelves: reading-again
This is hitherto the only enjoyable book on statistics that I've ever read ... and because of that it has made statistics a much more enjoyable subject for me.

The format is a good balance between well-written prose and equations, which works well with my learning style.

The example datasets that are worked through are helpfully instructive because they contain sources of potential pitfalls, like multicollinearity, which are then highlighted and used as lessons for things to watch out for in your
...more
Joe Suzuki
Feb 01, 2017 rated it it was amazing
I used this book for my course (undergraduate math dept) at Osaka University, a top-five university in Japan. The book is written in English and few students read the book while I explained the contents in Japanese in the class. I found the presentation including many figures and excluding equations (the discussion is mathematically sound) is very impressive and rather comfortable. I really recommend to read the book first rather than "Elements of Statistical Learning ".

(Currently, I am too busy
...more
Truc-Vien Nguyen
Oct 20, 2014 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.
Joe
Dec 09, 2015 rated it really liked it
Maybe the best overview and handbook for a data scientist / statistician on the most common statistical methods. It had the right level of technical expertise for what I wanted (enough to know how the algorithm worked, but not so much that I felt the book was telling me how to program R from scratch). Good at showing why you'd want to use one algorithm over another.
Nancy
Mar 10, 2014 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
Michael Regier
Nov 08, 2016 rated it really liked it
An excellent introduction to the field.
Marcus
May 19, 2019 rated it it was amazing
Wonderful introduction.
Darian
Feb 22, 2020 rated it it was amazing
Amazing book, I can't remember the last time i've seen a book explain relatively complex topics such as learning algorithms as straightforwardly as ISLR has. The exposition is clear, and only really expects the reader to maybe have some knowledge of calculus, linear algebra, and statistics, but you really don't need any or all of those at once, they just help aid in understanding.

This book genuinely reinvigorated my interest in machine learning/statistical learning. I've been having a bit of a
...more
Thanh Tùng
Feb 20, 2018 rated it it was amazing
Shelves: data-science
A great introduction book for statistical learning, a closely related field to machine learning. This is the accompany book for the course with the same name by Stanford University online MOOC platform. The content is intended for the beginners in machine learning therefore much less math than the other book - Element of Statistical Learning. The professors are actually the authors of several most popular algorithms (boostrap, ridge and lasso regression,...) and many R packages that we are using ...more
Anthony
May 10, 2020 rated it liked it
Shelves: coding
I liked the books clear explanations of what was going on with each procedure. There was not a lot of math, but some. More math probably would have helped in places, but for the audience they are aiming at, I can see why they left it out.

They did stress the idea that no one procedure was best at everything. The exercises were good about drilling this in.

I did have some problems with the book. The use of the equal sign for assignments (instead of <-) was somewhat off-putting. (Does that make me
...more
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