An Introduction To Statistical Learning With Applications In R
by Gareth James · first published 2013
87% recommend13% neutral
Why HN recommends it
Beginner-friendly, free introduction to machine learning from a statistics angle, needing no formal stats background. Works as a practical companion to the more advanced Elements, with hands-on R exercises.
Summarised by Claude from recommending comments
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, and more. Color graphics and real-worl
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I really like Introduction To Statistical Learning with Applications in R, by Daniela Witten, Gareth James, Robert Tibshirani, and Trevor Hastie. It's the easiest book I've found to get into machine learning without any formal statistics training.
on “Ask HN: What's the best computer science book you've read recently?” ↗Two free books that I haven't seen mentioned, that are from more of a stats perspective * James, Witten, Hastie, and Tibshirani's An Introduction to Statistical Learning, with Applications in R http://www-bcf.usc.edu/~gareth/ISL/ * Hastie, Tibshirani, and Freedman's Elements of statistical learning (more advanced) http://statweb.stanford.edu/~tibs/ElemStatLearn/
on “Level-Up Your Machine Learning” ↗Its companion book is also very nice (and way easier for a beginner): An Introduction to Statistical Learning with Applications in R. It is one of the only textbooks I have read from cover to cover in my life.
on “Essential Cheat Sheets for Machine Learning and Deep Learning Engineers” ↗Similarly, An Introduction to Statistical Learning With Applications in R is like a practical version of (or companion to) Elements. I very much enjoyed it.
on “Free Data Science Books” ↗…I'm currently taking the Stanford data mining class, STATS202. The book we're using has been really great (published this year) and covers a great deal more than this site seems to. It's called "An Introduction to Statistical Learning with Applications in R." It's free online through the Stanford libraries, but I'm not sure about accessing it for free elsewhere. The lectures are also probably recorded online somewhere, if anyone is really interested.
on “A Programmer's Guide to Data Mining” ↗The Elements of Statistical Learning together with the online course (http://www.r-bloggers.com/in-depth-introduction-to-machine-l...) makes for a great introduction. EDIT: Oops I should have said "An Introduction to Statistical Learning with Applications in R" rather than The Elements of Statistical Learning. The Elements book goes into way too much depth to be a good introduction to the subject.
on “Free Data Science Books” ↗Thanks for sharing. I also recommend "An Introduction to Statistical Learning - with Applications in R": http://www-bcf.usc.edu/~gareth/ISL/
on “From Linear Models to Machine Learning (draft) [pdf]” ↗An Introduction to Statistical Learning with Applications in R http://www-bcf.usc.edu/~gareth/ISL/ download the pdf book: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Fourth%20Printing....
on “Statistical Learning” ↗I found "An Introduction to Statistical Learning with applications in R" to be very useful.
on “Ask HN: How can a front-end developer dive into machine learning?” ↗Introduction to Statistical Learning with Applications in R http://www-bcf.usc.edu/~gareth/ISL/ Statistical Rethinking by Richard McElreath gives a good introduction to Bayesian approaches to statistical analysis https://www.youtube.com/channel/UCNJK6_DZvcMqNSzQdEkzvzA
on “Ask HN: Best way to get started with AI?” ↗The Elements of Statistical Learning or the more beginner friendly An Introduction to Statistical Learning: With Applications in R
on “Ask HN: Which books are “must-read” for anyone trying to learn about your field?” ↗While there are some good picks (especially "An Introduction to Statistical Learning with Applications in R" and "Deep Learning with Python" by Chollet, the Keras author), I am surprised it is missing "Information Theory, Inference, and Learning Algorithms" by David MacKay (http://www.inference.org.uk/itila/book.html).…
on “Best Data Science Books According to the Experts” ↗ISLR (aka "An Introduction to Statistical Learning: With Applications in R") is a great book on the principles of machine learning (including regression). And you get practical experience using R to actually implement such applications.
on “Good old fashion ML is dying” ↗1. Get An Introduction to Statistical Learning with Applications in R/Python (aka ISLR/ISLP) by Hastie et al. Read this from cover-to-cover and make sure that you understand the concepts/ideas/nuances/subtleties explained. 2. Keep a couple of Mathematics/Statistics books…
on “Ask HN: Daily practices for building AI/ML skills?” ↗…i found the following really helpful to understand AI/ML from the basics; 1) Neural Networks for Applied Sciences and Engineering by Sandhya Samarasinghe. Old book but great for understanding. 2) An Introduction to Statistical Learning: With Applications in R or Python by Hastie, Witten et al. 3) Any Statistics and Probability textbook should be at hand for reference.
on “Ask HN: What skills do you want to develop or improve in 2025?” ↗