> For the complete documentation index, see [llms.txt](https://gdsc-siesgst.gitbook.io/resource-hub/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gdsc-siesgst.gitbook.io/resource-hub/ml-resources.md).

# ML Resources

#### Data Cleaning & Mining

* [A Brief Introduction to Machine Learning for Engineers](https://drive.google.com/file/d/14y6IPpgYUC54ugTsKK4e4oUupXuvA38w/view?usp=sharing) - Ihab F.Illyas, Xu Chu (PDF)
* [Data Mining - The Textbook](https://drive.google.com/file/d/1Skr308Tfh-v9OFsQ_61GUl5kSUZdtOVV/view?usp=sharing) - Springer(PDF)

#### Machine Learning

* [A Brief Introduction to Machine Learning for Engineers](https://arxiv.org/pdf/1709.02840.pdf) - Osvaldo Simeone (PDF)
* [A Brief Introduction to Neural Networks](http://www.dkriesel.com/en/science/neural_networks)
* [A Course in Machine Learning](http://ciml.info/dl/v0_9/ciml-v0_9-all.pdf) (PDF)
* [A First Encounter with Machine Learning](https://www.ics.uci.edu/~welling/teaching/ICS273Afall11/IntroMLBook.pdf) (PDF)
* [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
* [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
* [Gaussian Processes for Machine Learning](http://www.gaussianprocess.org/gpml/)
* [Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/)
* [Introduction to Machine Learning](http://arxiv.org/abs/0904.3664v1) - Amnon Shashua
* [Learn Tensorflow](https://bitbucket.org/hrojas/learn-tensorflow) - Jupyter Notebooks
* [Learning Deep Architectures for AI](https://mila.quebec/wp-content/uploads/2019/08/TR1312.pdf) (PDF)
* [Machine Learning](http://www.intechopen.com/books/machine_learning)
* [Machine Learning, Neural and Statistical Classification](http://www1.maths.leeds.ac.uk/~charles/statlog/)
* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)
* [Probabilistic Models in the Study of Language](http://idiom.ucsd.edu/~rlevy/pmsl_textbook/text.html) (Draft, with R code)
* [Reinforcement Learning: An Introduction (Draft)](https://drive.google.com/file/d/1opPSz5AZ_kVa1uWOdOiveNiBFiEOHjkG/view) - Richard S. Sutton, Andrew G. Barto (PDF)
* [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 Python Game Book](http://thepythongamebook.com/en%3Astart)