Definitely depends on your math background (knowledge of analysis and linear algebra seems to be particularly helpful).

Witten's Data Mining is a very good beginner's book (has very little math, but lots of good explanations and discussion of real life issues).

Bishop's book is excellent, but it's easy to get lost if you don't have the mathematical background.

Duda, Hart & Stork's Patten Recognition book is also very well organized and has one of the best first chapters in any machine learning book. But it too requires mathematical background to be fully appreciated.

Hastie & Tibshirani's book is written by people from a statistical background, and is very very mathematical. I haven't progressed beyond Chapter 2, and I'm working on improving my math skills before I get back into it.

-- For NLP, a very good intro is the NLTK book.

Jurafsky & Martin's book covers more NLP topics, but Manning and Schutze cover statistical portions in depth. I think you should just read both :D.

Pattern Recognition and Machine Learning, Bishop http://www.amazon.com/Pattern-Recognition-Learning-Informati...

Note that this is a book for the more math-inclined, one will have trouble if not familiar with the basics of multivariate analysis/linear algebra.

Seems like a good book - bit surprised that it doesn't seem to cover Genetic Algorithms.

Genetic Algorithms are rarely covered in (recent) Machine Learning books. They are more often considered as optimization algorithms.

NLP:

http://www.amazon.com/Foundations-Statistical-Natural-Langua...

http://www.cs.colorado.edu/~martin/slp2.html

(and the python NLTK book is not bad)

Some of the other books recommended here are more comprehensive, but I found Mitchell's book better at teaching concepts.

http://www.cs.cmu.edu/~tom/mlbook.html

Simple things, like actually spelling out the steps of a derivation vs. "Here's equation a, from which we trivially derive equation b" but it might take you a while to figure out the steps if you haven't done a lot of Calculus problems recently.

And Mitchell just seems really good at explaining things (having heard him speak in person a few times).

I guess I could recommend to you 'Introduction to Machine Learning' by Ethem Alpaydin : http://mitpress.mit.edu/catalog/item/default.asp?tid=10341&#...

I find it is less brutal than Bishop's book, providing insightful analogies. It won't make you an expert, but it will give you a solid starting point.

If you later want to drill down more into reinforcement learning specifically, check out "Reinforcement Learning: An Introduction" by Sutton & Barto, full version available online in HTML form:

http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html

I'm currenty reading 'Machine Learning, an algorithmic perspective' by Marsland. He uses Python and assumes no higher math knowledge. I would recommend it to get a basic and intuitive understanding. He explains things in English, then presents the math notation, then explains the math notation in English again. Then, he provides Python code, and explains the Python code in English. Radical, I know.

I'm also using http://www.nltk.org/ NLTK to do document classification for a client right now, using insights gleaned from his book and also metaoptimzie (http://metaoptimize.com/qa).

Douglas Hofstadter, Fluid Concepts and Creative Analogies. This book will blow your mind.

http://en.wikipedia.org/wiki/Fluid_Concepts_and_Creative_Ana...