More Examples and Learning Resources

Much of my knowledge of statistics and machine learning comes from reading and working through books. This means completing every exercise I find interesting or do not immediately know how to solve, filling gaps in my knowledge, and extending topics presented in the book. I’ve worked through the following books:

• Introduction to Machine Learning with Python (2016) by Andreas Müller and Sarah Guido

• Python for Data Analysis (2017) by Wes McKinney

• Statistics (2007) by David Freedman, Robert Pisani, and Roger Purves

• Introduction to Statistical Learning (2013) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani

• Linear Models with R (2014) by Julian Faraway

• Mathematical Statistics with Applications (2008) by Dennis Wackerly, William Mendenhall, and Richard Scheaffer

My complete accompaniments for the last two books, totaling over 300 pages, can be found here.