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Machine Learning and Data Mining Books - A Baker's Dozen for Data Scientists

Here are 13 books on Machine Learning and Data Mining that are great resources, references, and refreshers for Data Scientists. (This is definitely a small selective subsample of the many excellent books available.)

  1. The Top Ten Algorithms in Data Mining, by Xindong Wu and Vipin Kumar (editors)
  2. Learning from Data, by Y.Abu-Mostafa, M.Magdon-Ismail, H-S.Lin
  3. Mining of Massive Datasets, by Jeffrey David Ullman and Anand Rajaraman
  4. Handbook of Statistical Analysis and Data Mining Applications, by G.Miner, J.Elder, R.Nisbet
  5. Machine Learning for Hackers, by Drew Conway and John Myles White
  6. Mahout in Action, by S.Owen, R.Anil, T.Dunning, E.Friedman
  7. Statistical and Machine-Learning Data Mining: Techniques for Better..., by Bruce Ratner
  8. Networks, Crowds, and Markets: Reasoning About a Highly Connected W..., by David Easley and Jon Kleinberg

  9. Bayesian Reasoning and Machine Learning, by David Barber
  10. Ensemble Methods in Data Mining: Improving Accuracy Through Combini..., by Giovanni Seni and John Elder (Older Edition is also available)

  11. Data Mining with R: Learning with Case Studies, by Luis Torgo
  12. Using R for Data Management, Statistical Analysis, and Graphics, by Nicholas Horton and Ken Kleinman
  13. Introduction to Data Mining, by P-N.Tan, M.Steinbach, V.Kumar

And for my astronomer friends, here are a couple of additional suggestions:

 14.  Statistics, Data Mining, and Machine Learning in Astronomy: A Pract..., by Z.Ivezic, A.Connolly, J.VanderPlas, A.Gray

 15.  Advances in Machine Learning and Data Mining for Astronomy, by M.Way, J.Scargle, K.Ali, A.Srivastava

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