An Introduction to Statistical Learning: with Applications in R Paperback – 25 Jun. 2013

This is one of the best books on the cutting edge between statistics and machine learning. I really enjoyed this book, it is accessible, easy to follow and full of knowledge. I don't really know how different the other book by the same authors "The Elements of Statistical Learning" is. They say that it is more thorough, but for what I need to do in my research this book is already enough. If you are not a mathematician, and you just need to apply data analytics to your research or in your job, this book will really help you. Heavier books on maths and stats with 500+ pages are not for me, as I generally get lost and find hard to follow those books. This one is not like that at all. It's a pleasure to read.

Honestly, this is the best statistics text I've ever read. Its so easy to understand and so engaging that once I start reading, its difficult to put the book down. The plots are very colourful and the book has useful R codes to implement the methods discussed. I believe this is one book every Data scientist should have on their shelf. I'm definitely going to read it over and over and over again.

Amazing book on the Fundamental algorithms governing the field of Machine/Statistical Learning. Simple , yet so effectively explained !

Key textbook for my MSc Machine Learning module. Uses standard R and covers the needed packages well. If you either have some statistics background or programming experience, self-study is also an option.Would be nice to have a chapter on using the tidyverse to simplify tasks.Nothing on cleaning data in here, you'll need another reference for that.

Although tagged as used, the book is basically new

An absolute classic of statistical aspects of machine learning methods! And very accessible introduction to all things statistical learning (and machine learning as you will discover).

Great read, really enjoying this book. It's expensive but worth it. (if you can get on with it, there are free PDFs I think)It' simple enough to understand, but complex enough to make me scratch my head and ponder about things..

Amazingly readable and concise. I bought the printed version with the hard cover, and the paper is of excellent quality.

This book is quite fantastic for an introductory level. I have a specialised training in mathematics and this books puts me up to date on several topics and algorithms without having to deal much time in the mathematical technicalities (for that, there are specialised treatises on each subject). The R code is quite good and although I think some of their advice is not appropriate (e.g. the attach function is in R only for legacy purposes but shouldn't be used since it overrides other functions, including base, aka default, functions), it is workable and so it's a great investment. The book is introductory focusing mostly on intuition and how to do, little time is spent in mathematical formalities, so that's a plus. I think there is a NEWER edition than the one I reviewed but otherwise I suspect the newer edition will be just better. Recommended.

This book is unparalleled in its coverage of Statistical/Machine Learning. It is comprehensive, easy to understand, and provides numerous examples to aid in comprehension. It also helps develop intuition and serves as a foundation for more mathematically advanced ML topics. Tip: Even if you prefer Python over R, consider using this book for theory and finding other resources for Python implementations.

Es un libro introductorio, pero que toca las bases matemáticas de los métodos estadísticos mas utilizados en análisis de datos.

Reprend les bases. Un livre que tout data scientist se doit de maitriser sur le bout des doigts. Très abbordable d'un point de vu mathématique.

Este livro contém a base de muitos métodos estatísticos utilizados em análise de dados: regressão, classificação, clusterização. Trata-se de uma introdução, sem matemática avançada, contendo os fundamentos que todo cientista de dados deveria saber.Dito isto, o livro é excelente, de leitura fácil e surpreendentemente agradável, sem deixar o rigor conceitual de lado. Há diversos exemplos e o encadeamento dos assuntos segue uma lógica bem interessante.Como bônus, ao final de cada capítulo há códigos e exercícios em R para que o leitor também possa "botar a mão na massa".