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# Great textbooks for machine learning (ML)!

Like classical statistics, ML is a set of tools that are extremely useful to have at your disposal as you try to better understand your data.

[If the statistics field had] incorporated computing methodology from its inception as a fundamental tool, as opposed to simply a convenient way to apply our existing tools, many of the other data related fields [such as ML] would not have needed to exist – they would have been part of statistics. – Jerry Friedman, taken from “Probabalistic Machine Learning” by Kevin Murphy

For many of the textbooks linked below, free versions of PDFs are likely buried on the internet!

## Introductory ML

- Applied Predictive Modeling
- note that predictions are not just about the future, but for any value you don’t know
- very applied, little to no math, great coverage of many topics (except not much detail on neural networks)
- good discussions of the tradeoffs between predictive accuracy and interpretability

- Introduction to Statistical Learning
- thorough, easy-to-read, beginner-friendly

- Deep Learning with Python
- great, simple intro to neural networks
- very practical, all code and
*no math* - I found some terms/concepts weren’t clearly defined (b/c no math) and neural nets are never visually described, which I find useful
- If you’re willing to google topics that you find insufficiently explained, this book is a great way to get started with neural networks and deep learning

## Less introductory ML

- Deep Learning
- drink from the firehose
- a few sections on more advanced topics were a bit hard to follow, but overall many gems here, especially the section on Structured Probabilistic Models

## Foundations

- Introduction to Linear Algebra
- linear algebra is the language of ML, and Gilbert Strang is
**the**person you should learn it from

- linear algebra is the language of ML, and Gilbert Strang is

## My favorite ML textbook

- Probabilistic Machine Learning
- not finished yet, but descriptions are wonderfully clear and concise
- while introductory, has more math/linear algebra than textbooks listed below
- has a follow up book that covers advanced topics