Goodreads helps you keep track of books you want to read.
Start by marking “An Introduction to Statistical Learning: With Applications in R” as Want to Read:
An Introduction to Statistical Learning: With Applications in R
Enlarge cover
Rate this book
Clear rating
Open Preview

An Introduction to Statistical Learning: With Applications in R

4.61  ·  Rating details ·  1,671 ratings  ·  145 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)
More Details... Edit Details

Friend Reviews

To see what your friends thought of this book, please sign up.

Community Reviews

Showing 1-30
Average rating 4.61  · 
Rating details
 ·  1,671 ratings  ·  145 reviews

More filters
Sort order
Start your review of An Introduction to Statistical Learning: With Applications in R
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
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
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.
Jul 07, 2019 rated it really liked it
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
Arian Jamasb
Aug 22, 2020 rated it it was amazing
Masterfully written. Clear, concise and lucid. Not to mention thrilling, truly thrilling.
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
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
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
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
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:
Jan 20, 2021 rated it it was amazing
It provides basic theoretical foundations in statistical learning for an introductory course of data science. Practical examples are good and help the students lay down concepts as well as keeping them motivated.

Obviously, there are things which are not covered here, but provides enough stuff to let the student to learn by himself.

An alternative edition with python would be fantastic.
Joaquin Menendez
Excellent book for anybody that wants to start adventuring in the marvelous world of data science
Terran M
Nov 18, 2017 rated it it was amazing
If you're going to read one book on statistical modeling, make it this one. When I teach data science to software engineers, this book is one of the cornerstones. I find that this book has just the right amount of theory for beginner, coupled with very useful R examples.

If you can read, understand, and practice this book, you will be employable as an entry-level data scientist.

One caveat is that I do not recommend emulating the authors' software practices, such as using attach() on data frames o
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. ...more
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
Oct 27, 2020 added it
gotta be honest, I did not read this whole textbook, but I've read enough over the year for it to count towards my reading goal thank you very much.

I've never reviewed a textbook before... um... very informative, helpful with assignments (thank you Gareth, Trevor, Robert, and Daniela), will actually be one of the few textbooks I hold onto so that's got to count for something, right?
Mehrzad M.
This book was a supplementary resource to DS Bootcamp. I read Chapters 1-5, 8-10.

Comprehending the concepts and mathematics behind supervised and unsupervised learnings for regression, classification, and clustering is the goal of this book, and it did a really great job. I wouldn't suggest reading it unless you practice the ideas and algorithms on appropriate data sets either in Python or R.
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
Shelves: non-fiction
I’m sure I will continue to review the content in this book time and time again as I continue practicing modeling!
Feb 03, 2019 rated it it was amazing
One of the finest intro ML books of our times.
Rohit Goswami
Jun 27, 2020 rated it it was amazing
This is a masterfully written book. There is of course, no better way to start with statistical learning than the brilliant tour-de-force of ISLR and ESL. I do personally find myself enjoying ESL more in some cases. This is easy to recommend, and a good introduction to statistics, especially in that it provides an even handed, "try things out first" approach. However, it would be a disservice to the community and authors to never delve into the details of the methods and techniques described. ...more
Anna-maria Rebel
Jan 12, 2020 rated it it was amazing
Amazing overview of the algebra behind machine learning methods applied to statistical problems!!!!
Mar 18, 2020 rated it it was amazing
Shelves: nonfiction, academic
An amazing book to start with the fundamentals of data science.
Mahammad Valiyev
Jan 08, 2021 rated it it was amazing
Shelves: 2020
A good book on statistical/machine learning. Focus is on intuition and practical sides. Also, the book is really comprehensive in terms of coverage of algorithms. Just would be nice to have Python implementations of labs on top of R.
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.
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
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
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
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. ...more
« previous 1 3 4 5 next »
There are no discussion topics on this book yet. Be the first to start one »

Readers also enjoyed

  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction
  • Deep Learning with Python
  • Hands-On Machine Learning with Scikit-Learn and TensorFlow
  • Pattern Recognition and Machine Learning
  • Deep Learning
  • The Hundred-Page Machine Learning Book
  • R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
  • Python for Data Analysis
  • Data Science for Business: What you need to know about data mining and data-analytic thinking
  • Introduction to Algorithms
  • Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems
  • Linear Algebra and Its Applications
  • Bayesian Data Analysis
  • Introduction to Machine Learning with Python: A Guide for Data Scientists
  • The Art of Statistics: How to Learn from Data
  • Introductory Econometrics: A Modern Approach
  • Cracking the Coding Interview: 150 Programming Questions and Solutions
  • Python Machine Learning
See similar books…

Goodreads is hiring!

If you like books and love to build cool products, we may be looking for you.
Learn more »

News & Interviews

Need another excuse to treat yourself to a new book this week? We've got you covered with the buzziest new releases of the day. To create our...
23 likes · 3 comments