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- 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/
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- 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
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