Fundamentals of Machine Learning:
	1. Introduction to Statistical Learning: with Appplications in R
	   Gareth James, Daniela Witten, Trevro Hastie and Robert Tibshiran
	   https://faculty.marshall.usc.edu/gareth-james/ISL/

	2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction
	   Trevor Hastie, Robert Tibshirani, and Jerome Friedman
	   https://web.stanford.edu/~hastie/ElemStatLearn/
       
	3. Pattern Recognition and Machine Learning 
	   Christopher Bishop

	4. Mathematics for Machine Learning
	   Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
	   https://mml-book.github.io/

	5. Deep Learning
	   Ian Goodfellow and Yoshua Bengio and Aaron Courville
	   https://www.deeplearningbook.org/

	6. Deep Learning from Scratch
	   Seth Weidman

Software & Coding:
	1. The Pragmatic Programmer
	   David Thomas, Andrew Hunt
	   https://pragprog.com/titles/ttp20/

	2. Refactoring
	   Martin Fowler, with Kent Beck

	3. R for Data Science
	   Garrett Grolemung and Hadley Wickham
	   https://r4ds.had.co.nz/

Practical Machine Learning:
	1. Hands-on Machine Learning with Scikit-Learn, Keras and Tensorflow
	   Aurelien Geron

	2. Deep Learning for Coders with fastai and PyTorch
	   Jeremy Howard and Sylvain Gugger

	3. Grokking Deep Learning
	   Andrew W. Trask
	   https://www.manning.com/books/grokking-deep-learning

	4. Natural Language Processing in Action: Understanding, analyzing, and generating text
	   Hobson Lane, Cole Howard, Hannes Hapke
	   https://www.manning.com/books/natural-language-processing-in-action

	5. Deep Learning with JavaScript

