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➕ add ML books and courses (#5726)

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* add The Mechanics of Machine Learning

* change url for Introduction to statistical Learning

* change url for Mining of Massive Datasets

* add Mathematics for Machine Learning course

* add Made with ML in courses

* fix position of Made with ML

* remove trailing

* fix trailing \ in programming-books-langs

* add PDF indication to MMDS book

* add author and remove author links
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2
books/free-programming-books-langs.md
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@ -1906,7 +1906,7 @@ That section got so big, we decided to split it into its own file, the [BY SUBJE
### R
* [Advanced R Programming](http://adv-r.had.co.nz) - Hadley Wickham
* [An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF)
* [An Introduction to Statistical Learning with Applications in R](https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF)
* [Cookbook for R](http://www.cookbook-r.com) - Winston Chang
* [Data Analysis and Prediction Algorithms with R](https://rafalab.github.io/dsbook/) - Rafael A. Irizarry
* [Data Analysis for the Life Sciences](https://leanpub.com/dataanalysisforthelifesciences) - Rafael A Irizarry, Michael I Love *(Leanpub account or valid email requested)*

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books/free-programming-books-subjects.md
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@ -216,7 +216,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA
* [Internet Advertising: An Interplay among Advertisers, Online Publishers, Ad Exchanges and Web Users](http://arxiv.org/pdf/1206.1754v2.pdf) (PDF)
* [Introduction to Data Science](https://docs.google.com/file/d/0B6iefdnF22XQeVZDSkxjZ0Z5VUE/edit?pli=1) - Jeffrey Stanton
* [Introduction to Data Science](https://leanpub.com/datasciencebook) - Rafael A Irizarry *(Leanpub account or valid email requested)*
* [Mining of Massive Datasets](http://www.mmds.org)
* [Mining of Massive Datasets](http://infolab.stanford.edu/~ullman/mmds/book.pdf) - Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman (PDF)
* [School of Data Handbook](http://schoolofdata.org/handbook/)
* [Statistical inference for data science](https://leanpub.com/LittleInferenceBook/read) - Brian Caffo
* [The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)](https://www.analyticsvidhya.com/blog/2018/08/dimensionality-reduction-techniques-python/) - Pulkit Sharma
@ -286,7 +286,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) (PDF)
* [A Selective Overview of Deep Learning](https://arxiv.org/abs/1904.05526) - Fan, Ma, and Zhong (PDF)
* [Algorithms for Reinforcement Learning](https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf) - Csaba Szepesvári (PDF)
* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
* [An Introduction to Statistical Learning](https://web.stanford.edu/~hastie/ISLR2/ISLRv2_website.pdf) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF)
* [Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage)
* [Deep Learning](http://www.deeplearningbook.org) - Ian Goodfellow, Yoshua Bengio and Aaron Courville
* [Deep Learning for Coders with Fastai and PyTorch](https://github.com/fastai/fastbook) - Jeremy Howard, Sylvain Gugger (Jupyter Notebooks)
@ -317,6 +317,7 @@ Books that cover a specific programming language can be found in the [BY PROGRA
* [Speech and Language Processing (3rd Edition Draft)](https://web.stanford.edu/~jurafsky/slp3/ed3book.pdf) - Daniel Jurafsky, James H. Martin (PDF)
* [The Elements of Statistical Learning](https://web.stanford.edu/~hastie/ElemStatLearn/) - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
* [The LION Way: Machine Learning plus Intelligent Optimization](https://intelligent-optimization.org/LIONbook/lionbook_3v0.pdf) - Roberto Battiti, Mauro Brunato (PDF)
* [The Mechanics of Machine Learning](https://mlbook.explained.ai) - Terence Parr and Jeremy Howard
* [The Python Game Book](http://thepythongamebook.com/en%3Astart)
* [Top 10 Machine Learning Algorithms Every Engineer Should Know](https://www.dezyre.com/article/top-10-machine-learning-algorithms/202) - Binny Mathews and Omair Aasim
* [Understanding Machine Learning: From Theory to Algorithms](https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning) - Shai Shalev-Shwartz, Shai Ben-David

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courses/free-courses-en.md
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@ -552,6 +552,8 @@
* [Machine Learning Recipes with Josh Gordon](https://www.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal)
* [Machine Learning Tutorial Python \| Machine Learning For Beginners](https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw) - Dhaval Patel
* [Machine Learning with Python by Saeed Aghabozorgi](https://cognitiveclass.ai/courses/machine-learning-with-python) (cognitiveclass.ai)
* [Mathematics for Machine Learning - Linear Algebra](https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3) - Imperial College London, Dr David Dye, Dr Sam Cooper
* [Mathematics for Machine Learning - Multivariate Calclus](https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23) - Imperial College London, Dr David Dye, Dr Sam Cooper
* [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book)
* [PyTorch tutorials by PyTorch.org](https://pytorch.org/tutorials)
* [Stanford University Machine Learning](https://www.coursera.org/learn/machine-learning)
@ -594,6 +596,7 @@
* [Introduction to Reinforcement Learning with David Silver](https://deepmind.com/learning-resources/-introduction-reinforcement-learning-david-silver) - David Silver
* [LouvainX Paradigms of Computer Programming – Abstraction and Concurrency](https://www.edx.org/course/paradigms-computer-programming-louvainx-louv1-2x-1#!)
* [LouvainX Paradigms of Computer Programming – Fundamentals](https://www.edx.org/course/paradigms-computer-programming-louvainx-louv1-1x-1)
* [Made with ML](https://madewithml.com) - Goku Mohandas (Applied ML · MLOps · Production)
* [MIT 6.S099: Artificial General Intelligence](https://agi.mit.edu)
* [MIT Numerical Methods (2014)](http://www.iitg.ernet.in/kartha/CE601-14/CourseSchedule.htm)
* [MIT's Artificial Intelligence](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)