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The following is a list of free, open source books on machine learning, statistics, data-mining, etc.
## Machine-Learning / Data Mining
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* [Real World Machine Learning ](https://manning.com/books/real-world-machine-learning ) [Free Chapters]
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* [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
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* [Think Bayes ](http://www.greenteapress.com/thinkbayes/ ) - Book + Python Code
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* [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 )
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* [Pattern Recognition and Machine Learning ](http://www.rmki.kfki.hu/~banmi/elte/Bishop%20-%20Pattern%20Recognition%20and%20Machine%20Learning.pdf )
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* [Machine Learning & Bayesian Reasoning ](http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf )
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* [Introduction to Machine Learning ](http://alex.smola.org/drafts/thebook.pdf ) - Alex Smola and S.V.N. Vishwanathan
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* [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/ )
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* [Practical Artificial Intelligence Programming in Java ](http://www.markwatson.com/opencontent_data/JavaAI3rd.pdf )
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* [Introduction to Machine Learning ](http://arxiv.org/pdf/0904.3664v1.pdf ) - Amnon Shashua
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* [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 )
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* [Introduction to Applied Bayesian Statistics and Estimation for Social Scientists ](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.177.857&rep=rep1&type=pdf ) - Scott M. Lynch
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* [Bayesian Modeling, Inference
and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf)
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* [A Course in Machine Learning ](http://ciml.info/ )
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* [Machine Learning, Neural and Statistical Classification ](http://www1.maths.leeds.ac.uk/~charles/statlog/ )
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* [Bayesian Reasoning and Machine Learning ](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage ) Book+MatlabToolBox
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* [R Programming for Data Science ](https://leanpub.com/rprogramming )
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* [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
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## Deep-Learning
* [Deep Learning - An MIT Press book ](http://www.deeplearningbook.org/ )
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## Natural Language Processing
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* [Coursera Course Book on NLP ](http://www.cs.columbia.edu/~mcollins/notes-spring2013.html )
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* [NLTK ](http://www.nltk.org/book/ )
* [NLP w/ Python ](http://victoria.lviv.ua/html/fl5/NaturalLanguageProcessingWithPython.pdf )
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* [Foundations of Statistical Natural Language Processing ](http://nlp.stanford.edu/fsnlp/promo/ )
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## Information Retrieval
* [An Introduction to Information Retrieval ](http://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf )
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## Neural Networks
* [A Brief Introduction to Neural Networks ](http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf )
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## Probability & Statistics
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* [Think Stats ](http://www.greenteapress.com/thinkstats/ ) - Book + Python Code
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* [From Algorithms to Z-Scores ](http://heather.cs.ucdavis.edu/probstatbook ) - Book
* [The Art of R Programming ](http://heather.cs.ucdavis.edu/~matloff/132/NSPpart.pdf ) - Book (Not Finished)
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* [All of Statistics ](http://www.ucl.ac.uk/~rmjbale/Stat/wasserman2.pdf )
* [Introduction to statistical thought ](https://www.math.umass.edu/~lavine/Book/book.pdf )
* [Basic Probability Theory ](http://www.math.uiuc.edu/~r-ash/BPT/BPT.pdf )
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* [Introduction to probability ](http://math.dartmouth.edu/~prob/prob/prob.pdf ) - By Dartmouth College
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* [Principle of Uncertainty ](http://uncertainty.stat.cmu.edu/wp-content/uploads/2011/05/principles-of-uncertainty.pdf )
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* [Probability & Statistics Cookbook ](http://matthias.vallentin.net/probability-and-statistics-cookbook/ )
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* [Advanced Data Analysis From An Elementary Point of View ](http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf )
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* [Introduction to Probability ](http://athenasc.com/probbook.html ) - Book and course by MIT
* [The Elements of Statistical Learning: Data Mining, Inference, and Prediction. ](http://statweb.stanford.edu/~tibs/ElemStatLearn/ ) -Book
* [An Introduction to Statistical Learning with Applications in R ](http://www-bcf.usc.edu/~gareth/ISL/ ) - Book
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* [Learning Statistics Using R ](http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/ )
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* [Introduction to Probability and Statistics Using R ](https://cran.r-project.org/web/packages/IPSUR/vignettes/IPSUR.pdf ) - Book
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* [Advanced R Programming ](http://adv-r.had.co.nz ) - Book
* [Practical Regression and Anova using R ](http://cran.r-project.org/doc/contrib/Faraway-PRA.pdf ) - Book
* [R practicals ](http://www.columbia.edu/~cjd11/charles_dimaggio/DIRE/resources/R/practicalsBookNoAns.pdf ) - Book
* [The R Inferno ](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf ) - Book
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## Linear Algebra
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* [Linear Algebra Done Wrong ](http://www.math.brown.edu/~treil/papers/LADW/book.pdf )
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* [Linear Algebra, Theory, and Applications ](https://math.byu.edu/~klkuttle/Linearalgebra.pdf )
* [Convex Optimization ](http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf )
* [Applied Numerical Computing ](http://www.seas.ucla.edu/~vandenbe/103/reader.pdf )
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* [Applied Numerical Linear Algebra ](http://uqu.edu.sa/files2/tiny_mce/plugins/filemanager/files/4281667/hamdy/hamdy1/cgfvnv/hamdy2/h1/h2/h3/h4/h5/h6/Applied%20Numerical%20Linear%20.pdf )
