update readme with more resources

README.md

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-# Machine Learning Books
-This repo is forked from [dynamicdeploy/gninrael-enihcam](https://github.com/dynamicdeploy/gninrael-enihcam).
+## Machine Learning Books
 
-It collects some machine learning books. It has not been updated for a while, this fork is just for keeping a fork of them.
+
+
+### Free to Download AI/Machine Learning Books
+
+> some are from https://github.com/brpy/ml-books
+
+| Book/Resource                          | Author(s) | Links|
+|----------------------------------------|----------------------------|-----------------------------------------------|
+| AI | Leonard | [[gitbook](https://leonardoaraujosantos.gitbook.io/artificial-inteligence/chapter1)] |
+| d2l-ai                                 | Community | [[github]](https://github.com/d2l-ai/d2l-en) [[pdf]](https://d2l.ai/d2l-en.pdf)|
+| Deep Learning with Pytorch             |Eli Stevens, Luca Antiga, Thomas Viehmann| [[pdf]](https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf)|
+| Ml Primer                              |Mihail Eric| [[pdf]](https://www.confetti.ai/assets/ml-primer/ml_primer.pdf) |
+| 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)|
+| Foundations of Data Science            |Avrim Blum, John Hopcroft, Ravindran Kannan| [[pdf]](https://www.cs.cornell.edu/jeh/book.pdf)|
+| Think Stats                            |Allen Downey|[[github]](https://github.com/AllenDowney/ThinkStats2) [[pdf]](https://greenteapress.com/thinkstats/thinkstats.pdf)|
+| Math4ml                                |Garrett Thomas|[[github]](https://github.com/gwthomas/math4ml) [[pdf]](https://gwthomas.github.io/docs/math4ml.pdf)|
+| 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)|
+| Think python 2                         |Allen Downey|[[pdf]](http://greenteapress.com/thinkpython2/thinkpython2.pdf)|
+| Intermediate python                    |Muhammad Yasoob Ullah Khalid|[[pdf]](https://buildmedia.readthedocs.org/media/pdf/intermediatepythongithubio/latest/intermediatepythongithubio.pdf)|
+| 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)|
+| Computer Age Statistical Inference     |Bradley Efron, Trevor Hastie|[[pdf]](https://web.stanford.edu/~hastie/CASI_files/PDF/casi.pdf)|
+| An Introduction to Statistical Learning |Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani|[[pdf]](https://statlearning.com/ISLR%20Seventh%20Printing.pdf)|
+| The Elements ofStatistical Learning    |Trevor Hastie, Robert Tibshirani, Jerome Friedman|[[pdf]](https://web.stanford.edu/~hastie/Papers/ESLII.pdf)|
+
+
+
+- [dynamicdeploy/gninrael-enihcam](https://github.com/dynamicdeploy/gninrael-enihcam). A collection of machine learning books, mainly updated in 2017.
+- [Free machine Learning Books](https://github.com/shahumar/Free-Machine-Learning-Books) A collection of books from @shahumar
+- [Awesome machine learning books](https://github.com/josephmisiti/awesome-machine-learning/blob/master/books.md)
+
+
+
+## Free Online Learning Courses
+
+|#| Course Provider               | URL                                                          | Review                                                       |
+|--| ----------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
+| 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. |
+| 2 | Stanford CS229                | http://cs229.stanford.edu/syllabus-autumn2018.html           |                                                              |
+| 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. |
+| 4 | Computer Sciense For AI (edx/HarvardX)| https://www.edx.org/professional-certificate/harvardx-computer-science-for-artifical-intelligence| On Edx|
+| ~ | There Are More |  |  |
+
+
+
+### More of Free Machine Learning/AI/Data Science Courses
+
+- https://github.com/luspr/awesome-ml-courses
+- https://github.com/josephmisiti/awesome-machine-learning/blob/master/courses.md Most of them are from Universities