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I'd just like to say, as a self-taught hacker, that I would pay $500 for a "_why's poignant guide to machine learning", and I'm sure I'm not alone.

What exactly do you want? Do you want a resource that "clicks" with you that you can use to learn about machine learning, or do you want a witty piece of art like "_why's poignant guide to ruby"?

If its the former you are looking for, I have several recommendations. There are a plethora of online resources to learn about machine learning from. In video form, my favorite resource is Yaser Abu-Mostafa's (Caltech prof) video lectures, available here: http://work.caltech.edu/teaching.html

You can also actually enroll in the next version of CS 156 (Abu-Mostafa's class) and do the class online with problem sets very similar to that of Caltech students actually enrolled in the class. (It starts January 8th).

Coursera/Udacity contain several classes that involve machine learning; Andrew Ng's class is a good place to start.

If you (like me) prefer text to video, you can buy Abu-Mostafa's book (I hear it follows the course very closely, and the book costs way less than $500 :D ), read Andrew Ng's online lecture notes, or read one of several freely available ML books (such as "Elements of Statistical Learning" or "Bayesian Reasoning and Machine Learning", but I've found these to take more mathematical maturity that the other resources recommended).

Note: One reason that I've highly recommended Abu-Mostafa's resources (besides having taken the class at Caltech and loving it) is that his class is more focused on the theory of machine learning (ie hammering in over-arching principles like avoiding overfitting by regularizing) rather than just covering as many algorithms as possible. Also, I believe Mitchell of CMU has good online resources for his machine learning course.

Definitely, something that 'clicks'.

_why's book for Ruby, pg/norvig's books for Lisp ... they're so enjoyable to read that you don't need to exert yourself for the subject to 'click', which is pretty impressive considering what you're learning.

I don't want to go to school to learn machine learning, but I'd be willing to pay money (maybe quite a lot of money) for a book that made it enjoyable.

For kernel-based methods in particular, Shawe-Taylor and Cristianini's "Kernel Methods for Pattern Analysis" (http://www.kernel-methods.net) is the textbook to use.

How about "Learn machine learning the hard way?"

I don't think that would be a good fit for teaching these things. Zed Shaw's series is primarily meant to teach concrete skills through rote memorization.

Machine learning isn't a "tool" you use to instantly "solve" a concrete problem you're having. Instead, you have to build up a large body of intuition about which tools you can use to work with your data. It can't be memorized.

What is it exactly that you'd like to know?

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