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3.3 out of 5 stars
3.3 out of 5 stars
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on February 16, 2016
very comprehensive. will be relevant for a long time to come.
there's a move for people to adopt the approach of learning coding libraries in order to solve problems...which is good but one still needs a reliable reference to fill in the blanks or to learn the basics (and advanced!).
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on November 6, 2015
The book is printed in black and white and also, some pages are crumpled.
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on August 27, 2015
the book is with poor print quality, not hard cover, and sent from India. Some pages are messed up as the ink was wet. I did not return it as I am leaving the city soon!!
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on July 13, 2015
I've used this book in a grad course of ML and even though it's an okay reference, it's hard to follow and the writing is far from clear. To be honest, it's boring, and god knows ML is not a boring subject! Unfortunately I don't know other books on the topic, but I've read much better math|stats|CS books (for contrast, check out Convex optimization by Boyd: that's pure clarity all the way). Still, it's a good reference to keep on your desk if google doesn't give you what you're looking for. However, if you wish to learn ML on your own, this one is probably not for you.
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on February 12, 2010
This is the textbook I'm using for an undergraduate machine learning course, and so far it has been very enjoyable. There are plenty of exercises in each chapter, from simple "derive a formula for ..." to more in depth problems, and several of the problems have solutions. One thing I've found really useful is how well referenced the book is within itself. It'll say something like "Recall that, if we assume a squared loss function, then the optimal prediction, for a new value of x, will be given by the conditional mean of the target variable". In the margin, it then has in red the text "Section 1.5.5" pointing you to where we learned this bit of trivia. Formatting is also well done, charts are colourful and seem to get the point across well. I find that I learn much easier by being shown a picture/graph of what we want to achieve, and then have it described, and finally being given the equations for solving this (rather than just being given the equations), which this book does well. It has taken a fair amount of work to get through though, and so I wouldn't say it's an easy textbook by any means (I mean, come on, we're teaching computers how to think, that can't be easy).

My one complaint is that I wish that they had a chapter/appendix with a bit of a stats refresher, because the last stats course I took was over a year ago, and so this textbook took me a little bit to get into for lack of knowing what some of the early terms meant.
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on July 18, 2008
This books provides an excellent introduction to a wide range of techniques in machine learning and also serves as a good reference.

However, it does require the reader to be mathematically mature, otherwise it can take some time to read through. It is probably best for someone at the graduate level (who has already taken a couple of graduate courses).
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on June 19, 2008
I've found this book a disaster for my Machine Learning course. It claims to use as little math as possible in the introduction, but as anyone that owns the book would tell you, a chapter could easily gather 200+ equations, and students would require pretty advanced calculus to actually have a clue on what the math's about.
The book does a poor job at conveying the ideas across, and although the professor reuses lots of graphics, his trimmed down notes were much more useful at an attempt to understand the topic.
Personally I found the book a waste unless you already have some understanding of machine learning in the first place. Beginners need not apply.
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