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For 434 | the avoidance of doubt, this paragraph does not form part of the 435 | public licenses. 436 | 437 | Creative Commons may be contacted at creativecommons.org. 438 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Machine Learning Books 2 | 3 | 4 | 5 | ### Free to Download AI/Machine Learning Books 6 | 7 | > some are from https://github.com/brpy/ml-books 8 | 9 | | Book/Resource | Author(s) | Links| 10 | |----------------------------------------|----------------------------|-----------------------------------------------| 11 | | AI | Leonard | [[gitbook](https://leonardoaraujosantos.gitbook.io/artificial-inteligence/chapter1)] | 12 | | d2l-ai | Community | [[github]](https://github.com/d2l-ai/d2l-en) [[pdf]](https://d2l.ai/d2l-en.pdf)| 13 | | Deep Learning with Pytorch |Eli Stevens, Luca Antiga, Thomas Viehmann| [[pdf]](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf)| 14 | | Ml Primer |Mihail Eric| [[pdf]](https://www.confetti.ai/assets/ml-primer/ml_primer.pdf) | 15 | | Mathematics For Machine Learning |Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong|[[github]](https://github.com/mml-book/mml-book.github.io) [[pdf]](https://mml-book.github.io/book/mml-book.pdf)| 16 | | Foundations of Data Science |Avrim Blum, John Hopcroft, Ravindran Kannan| [[pdf]](https://www.cs.cornell.edu/jeh/book.pdf)| 17 | | Think Stats |Allen Downey|[[github]](https://github.com/AllenDowney/ThinkStats2) [[pdf]](https://greenteapress.com/thinkstats/thinkstats.pdf)| 18 | | Math4ml |Garrett Thomas|[[github]](https://github.com/gwthomas/math4ml) [[pdf]](https://gwthomas.github.io/docs/math4ml.pdf)| 19 | | Think bayes |Allen Downey|[[github]](https://github.com/AllenDowney/ThinkBayes) [[html]](http://www.greenteapress.com/thinkbayes/html/index.html) [[pdf]](http://www.greenteapress.com/thinkbayes/thinkbayes.pdf)| 20 | | Think python 2 |Allen Downey|[[pdf]](http://greenteapress.com/thinkpython2/thinkpython2.pdf)| 21 | | Intermediate python |Muhammad Yasoob Ullah Khalid|[[pdf]](https://buildmedia.readthedocs.org/media/pdf/intermediatepythongithubio/latest/intermediatepythongithubio.pdf)| 22 | | Pattern Recognition and Machine Learning |Christopher Bishop|[[pdf]](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf)| 23 | | Computer Age Statistical Inference |Bradley Efron, Trevor Hastie|[[pdf]](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)| 24 | | An Introduction to Statistical Learning |Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani|[[pdf]](https://statlearning.com/ISLR%20Seventh%20Printing.pdf)| 25 | | The Elements ofStatistical Learning |Trevor Hastie, Robert Tibshirani, Jerome Friedman|[[pdf]](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)| 26 | 27 | 28 | 29 | - [dynamicdeploy/gninrael-enihcam](https://github.com/dynamicdeploy/gninrael-enihcam). A collection of machine learning books, mainly updated in 2017. 30 | - [Free machine Learning Books](https://github.com/shahumar/Free-Machine-Learning-Books) A collection of books from @shahumar 31 | - [Awesome machine learning books](https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md) 32 | 33 | 34 | 35 | ## Free Online Learning Courses 36 | 37 | |#| Course Provider | URL | Review | 38 | |--| ----------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | 39 | | 1 | Kaggle | https://www.kaggle.com/learn | From Beginning to Advanced, Kaggle offered many AI courses including basic Python Programming to Deep Learning in very practical way. | 40 | | 2 | Stanford CS229 | http://cs229.stanford.edu/syllabus-autumn2018.html | | 41 | | 3 | IBM Data Science Professional | https://www.coursera.org/professional-certificates/ibm-data-science | On Coursera. This is not free for certificate, but most of course materials are free. | 42 | | 4 | Computer Sciense For AI (edx/HarvardX)| https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence| On Edx| 43 | | ~ | There Are More | | | 44 | 45 | 46 | 47 | ### More of Free Machine Learning/AI/Data Science Courses 48 | 49 | - https://github.com/luspr/awesome-ml-courses 50 | - https://github.com/josephmisiti/awesome-machine-learning/blob/master/courses.md Most of them are from Universities 51 | --------------------------------------------------------------------------------