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# An Introduction to Statistical Learning: With Applications in R

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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

## Get A Copy

Hardcover, 426 pages

Published
September 1st 2017
by Springer
(first published June 24th 2013)

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Start your review of An Introduction to Statistical Learning: With Applications in R

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

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

Mar 18, 2018
Lord_Humungus
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

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

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.

Feb 18, 2019
Joaquin Menendez
added it

Excellent book for anybody that wants to start adventuring in the marvelous world of data science

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 ...more

Oct 27, 2020
Lara
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?

...more

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?

...more

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. ...more

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

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

(Currently, I am too busy ...more

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