Some free ebooks to learn Machine Learning and related fields
Statistics
- Cameron Davidson-Pilon, Probabilistic Programming & Bayesian Methods for Hackers,
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Springer
Machine Learning
- Jeffrey Stanton, An Introduction to Data Science version 3 , 2013
- Nils J. Nilsson , Introduction to Machine Learning, http://ai.stanford.edu/~nilsson/mlbook.html
- Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html
- David Barber, Bayesian Reasoning and Machine Learning, http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online
- Mohit Deshpande, Pablo Farias Navarro, Machine Learning for Human Beings, https://pythonmachinelearning.pro/free-ebook-machine-learning-for-human-beings/
- Alex Smola and S.V.N. Vishwanathan, Introduction to Machine Learning, http://alex.smola.org/drafts/thebook.pdf
- Allen B. Downey, Think Stats, Probability and Statistics for Programmers, O’Reilly,
- Allen B. Downey., Think Stats 2nd Edition, http://greenteapress.com/wp/think-stats-2e/,
- Allen B. Downey, Think Bayes,
- Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning, [Website]
- Ron Zacharski, A Programmer’s Guide to Data Mining, http://guidetodatamining.com/
- Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman, Mining of Massive Datasets http://infolab.stanford.edu/~ullman/mmds/book.pdf
- Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python, http://www.nltk.org/book_1ed/
- Richard Szeliski, Computer Vision: Algorithms and Applications, http://szeliski.org/Book/
- Hal Daume, A Course in Machine Learning, http://ciml.info/
- Max Welling, A First Encounter with Machine Learning, https://www.ics.uci.edu/~welling/teaching/273ASpring10/IntroMLBook.pdf
- Carl Edward Rasmussen and Christopher K. I. Williams, Gaussian Processes for Machine Learning, http://www.gaussianprocess.org/gpml
- Amnon Shashua, Introduction to Machine Learning, [PDF]
- Julie Steele, Understanding the Chief Data Officer, O’Reilly,
- Ron Zacharski, A Programmer’s Guide to Data Mining, 2015
- D. Michie, D.J. Spiegelhalter, C.C. Taylor (eds), Machine Learning, Neural and Statistical Classification
- David MacKay, Information Theory, Pattern Recognition and Neural Networks
- Jake VanderPlas, Python Data Science Handbook
- Andriy Burkov, Machine Learning Engineering [Draft]
- Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006 [PDF]
Data Engineering
- DJ Patil, Hilary Mason, Data Driven Creating a Data Culture, [Website], O’Reilly
- DJ Patil, Data Jujitsu: The Art of Turning Data into Product
- DJ Patil, Building Data Science Teams, O’Reilly
Neural Network
- David Kriesel, A Brief Introduction to Neural Networks, [PDF]
- Martin T. Hagan, Howard B. Demuth, Mark H. Beale, Orlando De Jess, Hagan, Neural Network Design 2nd Edition
Deep Learning
- Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning,
- Michael Nielsen, Neural Networks and Deep Learning, http://neuralnetworksanddeeplearning.com/
- Mohit Deshpande, Deep Learning with Python for Human Beings, https://pythonmachinelearning.pro/free-ebook-deep-learning-with-python/
Sources
- Big Data Made Simple: Learning more like a human: 18 free eBooks on Machine Learning
- https://github.com/TechBookHunter/Free-Machine-Learning-Books
- https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html
- KD Nuggets: 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python R and more
- Free Machine Learning Books https://github.com/TechBookHunter/Free-Machine-Learning-Books
- Free Deep Learning Books, https://github.com/TechBookHunter/Free-Deep-Learning-Books
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