Amazon.ca:Customer reviews: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

Top positive review

Great introductory survey to many statistical tools and R code

Reviewed in Canada 🇨🇦 on February 1, 2022

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.

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Sandeep Sony

Old edition

Reviewed in Canada 🇨🇦 on November 3, 2020

I bought this as a used book, however, the book was an old edition and had errors, so returned it.

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From Canada

Demetri Pananos

Good introduction, but the book lacks details.

Reviewed in Canada 🇨🇦 on July 5, 2016

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This book is a gentle introduction to the realm of statistical learning. Aimed at practitioners of disciplines other than mathematics, the book gives an overview of learning and estimation methods for a wide variety of problems.

However, I struggle to see who in actuality would benefit from this book. The topics are not so easy to understand that just anyone can read the book. Topics require knowledge of linear algebra, probability theory, multivariable calculus, and an intimate knowledge of statistical methods. So, if readers are to be familiar with these topics, why forgo matrix notation in the chapters for multiple regression? Why omit various proofs of equalities? Why feel the need to "dumb down" the book?

In summation, the book would serve people familiar with mathematics well if they seek to familiarize themselves with various statistical learning procedures (like cross validation, boot strapping, and regularization). Curious readers will need seek more thorough explanations of the concepts elsewhere.

4 people found this helpful

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Chris Czerwinski

Great examples with R coding

Reviewed in Canada 🇨🇦 on November 9, 2020

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I loved the R coding especially to newcomers learning Statistics. What would have even been better is an Appendix on how to setup a small data base of a matrix or table of 10 by 5 elements using R.

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R. Garnica

Good book if you have a strong foundation in math

Reviewed in the United States 🇺🇸 on August 28, 2020

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This book covers most of the primary techniques used in data science and machine learning. Each chapter is devoted to a topic and explained further throughout sections within the chapter.

I don't quite have the mathematical foundation I need to get the most out of this book. For example, in reading chapter 3 on linear regression, I was following along just fine but once all the mathematical formulas got more complex page after page, I was lost. I realized I don't have the proper grounding in match to follow along.

If you're someone like me with a poor math foundation, it will prove to be a difficult hurdle to overcome as you cover the book. (I believe they still offer a free PDF version on their site, so take a quick perusal and if you find yourself crosseyed from the myriad of formulas presented, then it's best to save it for a later purchase.)

If you've got strong math skills, then this book will be a joy to read. I need someone more basic in terms of explaining not only the symbology used, but how the formulas are derived.

Hopefully I can find something more "beginner" and "basic" to guide me along so I can finally use this book for all it has to offer.

One person found this helpful

kirk

Paper quality is good, but can be better!

Reviewed in India 🇮🇳 on March 19, 2022

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Great content! Good paper - but I got the ESLR too, and the paper quality on that one beats the one here by a mile, so, this book can have a better paper quality and binding too. Still, it is fairly ok.

2 people found this helpful

Sathish Sanjeevi

Nice book. Some overkill features reduce readability in

Reviewed in the United States 🇺🇸 on April 12, 2021

Verified Purchase

As someone with a PhD, I find this book to be too basic but gets the job done well. I gave 4 stars since there are some features which seem to be a overkill in terms of readability. For example, different texts are highlighted in different colors and I find them to be distracting. To highlight some, section names are highlighted in blue, variable names in brown and regular text in black. Furthermore, there is normal font, italicized font and then courier font, all in same page, and this combined with variety of colors is a disaster. I would say KIS - keep it simple.

Anish Joshi

Book binding ripped

Reviewed in the United States 🇺🇸 on October 3, 2021

Verified Purchase

I received this rental with book binding ripped as can be clearly seen in the photo.

Mike MOrgan

A fine piece of work

Reviewed in the United States 🇺🇸 on December 31, 2016

Verified Purchase

Professor Hastie and his team have my utmost respect. They have brought data science into the mainstream more than anyone else I know. And this is their first "introductory" book, and the price was certainly right. Was this your "training sample," Dr. Hastie?

The treatment was good, but it would have been better by making sure the reader already knows probability theory fairly well. Or else, don't explain things in those terms. Also, where is the chapter on simulation and issues associated with that (like outliers)?

Overall, a fine piece of work, something that I'm sure will shine in the second edition (not printing, but edition). In the meantime, it sure helped me give a comprehensive tutorial to about three dozen data scientists in India, most of whom were not fully versed in Western culture or language.

Camilo Rodrguez

Excellent book for getting ready for machine learning implementations

Reviewed in the United States 🇺🇸 on May 4, 2017

Verified Purchase

This book is indispensable for whoever wants to start with machine learning with solid foundations. It gets rid of the deep mathematical developments of "The Elements of Statistical Learning" (another great book), emphasizing the concepts and techniques. The labs and exercises in R are a superb addition, giving you immediate hands-on practice as a start for doing your own exploration.

The only negative aspect of this book is the total absence of references and bibliography. Not even the authors and researchers who have shaped the field are mentioned anywhere. The inclusion of a References section at the end of each chapter would have been very valuable for persons which want to go deeper in some topics, and it wouldn't have increased significantly the length of the book..

A great book overall.

2 people found this helpful

Max

ISL is not a footstep for ESL

Reviewed in the United States 🇺🇸 on June 4, 2017

Verified Purchase

This book will not help you understand the ESL book (Elements of Statistical Learning).

If you are already programming ML a lot and you want to step up your ML math but find ESL too hard because it is not self-contained and uses too much graduate stats terminology then do not fall for the reviewers that recommend reading ISL (Introduction to Statistical Learning) instead. ISL does not contain explanations missing from ESL. In fact, it does not explain math at all, but instead, it gives a very broad overview of statistical methods that overlap with ML.

Then who is this book for? This book is for someone who juuust started learning ML, like completed the coursera ML course or started using Python scikit-learn.

The book is well-written though. It is not self-contained because it does not explain math but merely gives a minimum intuition behind it.

83 people found this helpful

Captain Miffles

Great textbook for ML algorithms (no deep learning stuff basically) in R

Reviewed in India 🇮🇳 on September 25, 2019

Verified Purchase

First off, this is an academic textbook so goes into details of the classical ML algorithms. There is no chapter on neural networks so keep that in mind.

That said, solid learning to be found here. It helped me clear a lot of misconceptions on basic stuff as Linear Regression etc.

It expects you to know R as its the defacto statistical programming language. I hope they had included Python code somewhere. But, you can always translate the pseudocodes to Python.

Printing quality is really good. Just a joy to read and learn from this book.

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