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on 29 July 2014
Having completed the Coursera Stanford Machine Learning course I wanted to know more and this came up at the top recommended book in Amazon for ML. I downloaded the free PDF but it's huge and I find it impossible to read a PDF on a screen so I forked out for the hardback paper copy. I have to say this is well worth it, incredible scope of coverage and the colouring makes it more easy to understand (none of this stuff is actually 'easy'). This IMO is genuinely THE bible for Machine Learning.
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on 8 November 2016
The content seems fine. It is comprehensive and judging from the first two chapters so far, the text is understandable, provided one has sufficient maths background.

My main complaint is about the truly awful print quality (second edition, seventh printing 2013). Many pages look smudged. For at least a dozen pages, the ink has gone right through the paper to make the overleaf pages completely unreadable.
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on 10 July 2012
This is a review of the 1st edition, though I have looked at a copy of the 2nd edition and as far as I can tell, it all still applies.

This is a genuinely excellent book. The authors thoroughly understand the topic and explain it with remarkable clarity. There are a good number of well thought out exercises and the book's website contains a lot of additional information and data.

If you really want to understand statistical learning methods, rather than just applying them blindly, then this book is definitely one to read.
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on 24 November 2016
Some context first: I'm studying my fourth year in a computer engineering program, having studied lightweight mathematics courses only, which is basically calculus, linear algebra, discrete mathematics and matematical statistics. Our machine learning course has two recommended literatures of which "The Elements of Statistical Learning" (ESL) was one of them, while the primary was Pattern Recognition and Machine Learning (PRML).

My experience with the book so far if very positive. It contains incredibly relevant machine learning methods/tools which many other books, most notably PRML, doesn't touch upon or at least explain very shortly, which are extensively used in practice. Most notably: Support Vector Machines, Random Forests and Ensemble Learning. Also, the structure of ESL has made a lot more sense to me compared to PRML, it wraps parts of the field into more easily digestible chunks, and therefore makes for a better reference than PRML (just compare the table of contents). Also, as the authors themselves point out, the book itself will rather the reader understands the intuition, algorithm and the cases in which they perform good/bad rather than the mathematical background/proofs behind them (don't worry, most of them are still presented in ESL though). In conclusion, if you can accept the skimming of proof and some rigour in ESL, this book is perfect, and summarizes a large part of the field in such a way that even a mathematically mediocre computer scientist is able to somewhat grasp and apply in real world problems. However, if you want to get the entire picture, you might want to read both ESL and PRML, which will give you some of that Bayesian goodies as well.
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on 11 January 2017
the authors are over 30 years masters in their work. it is hardto make a better overview of statistical learning (or machine learning, data science).
beside the formulas and tables there are many figures, in color. these figures give a very good idea how al types of analyses work. After reading a lot of books in this field one can say that this is the best.
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on 23 February 2017
The book itself was great. However, I bought it "as good as new", and... it definitely wasn't. Most pages are fine, but two whole chapters had their pages wrinkled and crumbled up. Moreover, the print on other pages is of questionable quality: e.g. not sharp, shifted colours etc.
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on 14 January 2011
This review is not meant to be a review for the paper edition of an otherwise excellent book, but it is only specific to the Kindle edition.
Obviously one can't expect the same quality of a printed book, but the quality of the Kindle edition is absolutely awful. All the mathematical formulas have been converted into some kind of bitmap images all of extremely poor quality, such that they are barely readable and sometimes completely wrong (i.e. different from what you read in the printed edition).
I don't know if this is a common problem for Kindle editions of books containing mathematical formulas/tables/images, but it is surprising that Amazon is selling this edition at more than £30: given the quality of the mathematical formulas, images and tables therein this book shouldn't be sold at all in the Kindle edition, not even given away for free!
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on 24 November 2016
I love it!
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on 12 December 2016
Very good
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on 14 May 2015
Very good book
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