The most popular choices seem to be:

Machine Learning: a Probabilistic Perspective, by Murphy

http://www.cs.ubc.ca/~murphyk/MLbook/

Pattern classification, by Duda et all

http://www.amazon.com/Pattern-Classification-Pt-1-Richard-Du...

The Elements of Statistical Learning, by Hastie et all. It is free from Stanford.

http://www-stat.stanford.edu/~tibs/ElemStatLearn

Mining of Massive Datasets, free from Stanford.

http://infolab.stanford.edu/~ullman/mmds.html

Bayesian Reasoning and Machine Learning, by Barber, free available online.

http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=...

Learning from data, by Abu-Mostafa.

It comes with Caltech video lectures: http://work.caltech.edu/telecourse.html

Pattern Recognition and Machine Learning, by Bischop

http://research.microsoft.com/en-us/um/people/cmbishop/prml/

Also noteworthy

Information Theory, Inference, and Learning Algorithms, by Mackay, free.

http://www.inference.phy.cam.ac.uk/itprnn/book.html

Classification, Parameter Estimation and State Estimation, by van der Heijden.

http://prtools.org

Computer Vision: Models, Learning, and Inference, by Prince, available for free

http://www.computervisionmodels.com/

Probabilistic Graphical Models, by Koller. Has an accompanying course on Coursera.

There was post on HN of a blog post link which contained a list of all free machine learning/data mining books. Wondering, if someone can post the link to it. I am unable to find it through search.

This one?

http://christonard.com/12-free-data-mining-books/

or this which includes good backgrounders on lin.alg, probability and stat,

http://www.reddit.com/r/MachineLearning/comments/1jeawf/mach...

Or http://www.electronicsforu.com/newelectronics/articles/hitsc...

this is an excellent review (but doesn't cover books by Mohri, Rostamizadeh, Talwalkar and Abu-Mostafa , Magdon-Ismail, Lin: http://www.amazon.com/review/R32N9EIEOMIPQU/ref=cm_cr_pr_per...

yes, yes. Thanks!

If you can afford it (both financially and regarding math background), Bishop is a really great choice. Almost everything you need to know is in it. I have it and just love it!

But he goes quite deep in the mathematical explanations (which is a great point, there is no better way to learn and understand) meaning you have to be willing to work on your math for this book.

It depends on your math background, but I've found Yaser S. Abu-Mostafa's Learning From Data the best, although Christopher M. Bishop's Pattern Recognition and Machine Learning is the gold standard. ;-)