> For the complete documentation index, see [llms.txt](https://aegion-dynamic.gitbook.io/learning-resources/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://aegion-dynamic.gitbook.io/learning-resources/machine-learning/recommended-books.md).

# Recommended Books

While countless resources exist to learn machine learning. Its important for learners to strengthen their mathematics foundations. This remains the single most important factor that can prevent learners from advancing beyond a certain point in their learning pathway. The books we recommend here will ensure that the learners build up the math literacy. We recommend working through the math sections from one of the textbooks (i.e. solve all chapter exercises).

1. An Introduction to Statistical Learning (<https://www.statlearning.com/>)
2. Probabilistic Machine Learning (<https://probml.github.io/pml-book/book1.html>)
3. Dive into Deep Learning (<https://d2l.ai/>)
4. Bayesian Reasoning and Machine Learning ([link](https://github.com/snowdj/CS228_PGM/blob/master/books/Bayesian%20Reasoning%20and%20Machine%20Learning%20by%20David%20Barber.pdf))
5. Reinforcement Learning - An Introduction (<https://mitpress.mit.edu/9780262039246/reinforcement-learning/>)
6. Mathematics for Machine Learning (<https://mml-book.github.io/book/mml-book.pdf>)
7. Pattern Recognition and Machine Learning (<https://www.microsoft.com/en-us/research/wp-content/uploads/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf>)