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    <p>I disagree with the people who recommend reading Programming Collective Intelligence.If you want to do anything of even moderate complexity, you need to be good at applied math and PCI gives you a false sense of confidence . For example, when it talks of SVM, it just says that libSVM is a good way of implementing them. Now libSVM is definitely a good package but who cares about packages. What you need to know is why SVM gives the terrific results that it gives and how it is fundamentally different from Bayesian way of thinking ( and how Vapnik is a legend) . </p> <p>IMHO , there is no one solution to it. You should have a good grip on Linear Algebra and probability and Bayesian theory . Bayes, i should add, is as important for this as oxygen for human beings ( its a little exaggerated but you get what i mean ,right ?) . Also, get a good grip on Machine Learning. Just using other people's work is perfectly fine but the moment you want to know why something was done the way it was, you will have to know something about ML. </p> <p>Check these two for that : </p> <p><a href="http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html" rel="nofollow">http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html</a></p> <p><a href="http://measuringmeasures.com/blog/2010/1/15/learning-about-statistical-learning.html" rel="nofollow">http://measuringmeasures.com/blog/2010/1/15/learning-about-statistical-learning.html</a></p> <p><a href="http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html" rel="nofollow">http://measuringmeasures.com/blog/2010/3/12/learning-about-machine-learning-2nd-ed.html</a></p> <p>Okay,now thats three of them :) / Cool </p>
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    1. POHow to get started on Information Extraction?
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