<commit_before>The following is a list of free, open source books on machine learning, statistics, data-mining, etc.

## Machine-Learning / Data Mining

* [An Introduction To Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Book + R Code
* [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) - Book
* [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book + IPython Notebooks
* [Think Bayes](http://www.greenteapress.com/thinkbayes/) - Book + Python Code
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/mackay/itila/book.html)
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/chapters/)
* [Data Intensive Text Processing w/ MapReduce](http://lintool.github.io/MapReduceAlgorithms/)
* [Reinforcement Learning: - An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
* [Mining Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf)
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf)
* [Pattern Recognition and Machine Learning](http://www.rmki.kfki.hu/~banmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf)
* [Machine Learning & Bayesian Reasoning](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - <NAME> and <NAME>
* [A Probabilistic Theory of Pattern Recognition](http://www.szit.bme.hu/~gyorfi/pbook.pdf)
* [Introduction to Information Retrieval](http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf)
* [Forecasting: principles and practice](http://otexts.com/fpp/)
* [Practical Artificial Intelligence Programming in Java](http://www.markwatson.com/opencontent_data/JavaAI3rd.pdf)
* [Introduction to Machine Learning](http://arxiv.org/pdf/0904.3664v1.pdf) - <NAME>
* [Reinforcement Learning](http://www.intechopen.com/books/reinforcement_learning)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [A Quest for AI](http://ai.stanford.edu/~nilsson/QAI/qai.pdf)
* [Introduction to Applied Bayesian
Statistics and Estimation for
Social Scientists](http://faculty.ksu.edu.sa/69424/us_BOOk/Introduction%20to%20Applied%20Bayesian%20Statistics.pdf)
* [Bayesian Modeling, Inference
and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
* [A Course in Machine Learning](http://ciml.info/)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) Book+MatlabToolBox
* [R Programming for Data Science](https://leanpub.com/rprogramming)

## Natural Language Processing<commit_msg>added book in machine learning<commit_after>The following is a list of free, open source books on machine learning, statistics, data-mining, etc.

## Machine-Learning / Data Mining

* [An Introduction To Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Book + R Code
* [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) - Book
* [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book + IPython Notebooks
* [Think Bayes](http://www.greenteapress.com/thinkbayes/) - Book + Python Code
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/mackay/itila/book.html)
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/chapters/)
* [Data Intensive Text Processing w/ MapReduce](http://lintool.github.io/MapReduceAlgorithms/)
* [Reinforcement Learning: - An Introduction](http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html)
* [Mining Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf)
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf)
* [Pattern Recognition and Machine Learning](http://www.rmki.kfki.hu/~banmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf)
* [Machine Learning & Bayesian Reasoning](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf)
* [Introduction to Machine Learning](http://alex.smola.org/drafts/thebook.pdf) - <NAME> and <NAME>
* [A Probabilistic Theory of Pattern Recognition](http://www.szit.bme.hu/~gyorfi/pbook.pdf)
* [Introduction to Information Retrieval](http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf)
* [Forecasting: principles and practice](http://otexts.com/fpp/)
* [Practical Artificial Intelligence Programming in Java](http://www.markwatson.com/opencontent_data/JavaAI3rd.pdf)
* [Introduction to Machine Learning](http://arxiv.org/pdf/0904.3664v1.pdf) - <NAME>
* [Reinforcement Learning](http://www.intechopen.com/books/reinforcement_learning)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [A Quest for AI](http://ai.stanford.edu/~nilsson/QAI/qai.pdf)
* [Introduction to Applied Bayesian
Statistics and Estimation for
Social Scientists](http://faculty.ksu.edu.sa/69424/us_BOOk/Introduction%20to%20Applied%20Bayesian%20Statistics.pdf)
* [Bayesian Modeling, Inference
and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
* [A Course in Machine Learning](http://ciml.info/)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) Book+MatlabToolBox
* [R Programming for Data Science](https://leanpub.com/rprogramming)
* [Data Mining - Practical Machine Learning Tools and Techniques](http://www.cse.hcmut.edu.vn/~chauvtn/data_mining/Texts/%5B7%5D%20Data%20Mining%20-%20Practical%20Machine%20Learning%20Tools%20and%20Techniques%20%283rd%20Ed%29.pdf) Book

## Natural Language Processing
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{'dir': 'git-commits-cleaned', 'id': '1899696', 'n_tokens_mistral': 2223, 'n_tokens_neox': 2017, 'n_words': 342}