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I want to learn machine learning. I found tons of material on the internet but couldn't decide which book to get started with.

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    $\begingroup$ My advice: don't rely on only one book. Or even two. One nice thing about ESLII is you can look at it online and see what you'd be in for. But if you're new to the topic, Introduction to statistical learning (ISL) might be a better first book than ESL. $\endgroup$
    – Glen_b
    May 30 '16 at 11:07
  • $\begingroup$ I'm not sure this needs to be closed. It should probably be made CW, though. $\endgroup$ May 30 '16 at 13:57
  • $\begingroup$ I agree with @gung ... it should be CW. $\endgroup$ May 30 '16 at 15:20
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    $\begingroup$ I agree with @Glen_b . It is a bad idea to go with just one. If you are an operator of one car, then you have one book on one car. If you are going to be in business as a mechanic, even a mechanic specialized on one manufacturers cars, then you should have a good library. If you want to be a mechanic in "Knuth's garage" or "Moore's Garage" then you should have libraries that inform working on their machinery. $\endgroup$ May 30 '16 at 15:25
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I would recommend the following:

  • "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. It is freely available as a PDF and a series of video lectures.
  • "Python Machine Learning" by Sebastian Raschka. It is very well-written, good combination of explanations and code, and the author is responsive.
  • "Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies", by John D. Kelleher, Brian Mac Namee and Aoife D'Arcy

For more advanced treatments:

  • "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" (as mentioned previously).
  • "Bayesian Reasoning and Machine Learning", by David Barber.
  • "Machine Learning", by Tom Mitchell.
  • "Machine Learning: A Probabilistic Perspective", by Kevin P. Murphy.
  • "Pattern Recognition and Machine Learning", by Christopher Bishop.
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Let me add this highly readable often-forgotten text,

Duda, R. O., Hart, P. E., & Stork, D. G. (2012). Pattern classification. John Wiley & Sons.

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In my opinion, one of the best: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., 2009, by Trevor Hastie, Robert Tibshirani, Jerome Friedman.

And you don't even have to buy it: http://web.stanford.edu/~hastie/ElemStatLearn/

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Principles of Data Mining by Hand, Mannila & Smyth is a good entry level text. It has chapters on data, visualizing, analyses and uncertainty, models/patterns, score functions, search and optimization, descriptive modeling, predictive modeling for classification, predictive modeling for regression, data organization, finding patterns and rules, retrieval by content, optimization, etc. It has been used for computer science students, as a background reading text.

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