Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.

University of Michigan - Ann Arbor

The list of helpful books about data science:
1. An Introduction to Statistical Learning with Applications in R
2.  The Elements of Statistical Learning: Data Mining, Inference and Prediction
3.  Learning From Data
4. The Hundred-Page Machine Learning Book by Andriy Burkov
5. The Deep Learning Revolution


Helpful data science books

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, pp. 3-7). New York: springer.

An Introduction to Statistical Learning with Applications in R

The Elements of Statistical Learning: Data Mining, Inference and Prediction

Abu-Mostafa, Y. S., Magdon-Ismail, M., & Lin, H. T. (2012). Learning from data (Vol. 4). New York, NY, USA:: AMLBook.

Learning From Data

The Hundred-Page Machine Learning Book is, in reality, about 130 pages. However, it covers a broad range of topics from introductory math (primarily vector and matrix transformations) to building statistical models to the new field of deep learning. It's very dense in content, so it's not like reading a typical 130-page book, but can be finished within a week or two. It's not a textbook so it doesn't have exercises to give you hands on experience, but it's very helpful in glossing a vast scope of machine learning concepts. 

Reference: Burkov, A. (2019). The Hundred-page Machine Learning Book. Leipzig: Andriy Burkov.

PDF Link: [themlbook.com/wiki/doku.php](https://themlbook.com/wiki/doku.php) 

The Hundred-Page Machine Learning Book by Andriy Burkov

Sejnowski, Terence J.  (2018). The Deep Learning Revolution. Cambridge, Massachusetts : The MIT Press.

Learn Before

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