John Myles White

John Myles White

Statistics and machine learning educator

  • @johnmyleswhite

Princeton, New Jersey

Areas of Expertise:

  • machine learning
  • statistics
  • data science
  • R
  • consulting
  • speaking
  • training
  • writing
John Myles White is a Ph.D. student in the Princeton Psychology Department, where he studies how humans make decisions both theoretically and experimentally. Outside of academia, John has been heavily involved in the data science movement, which has pushed for an open source software approach to data analysis. He is also the lead maintainer for several popular R packages, including ProjectTemplate and log4r.

Bandit Algorithms for Website Optimization Bandit Algorithms for Website Optimization
by John Myles White
December 2012
Print: $19.99
Ebook: $16.99

Machine Learning for Hackers Machine Learning for Hackers
by Drew Conway, John Myles White
February 2012
Print: $49.99
Ebook: $42.99

Machine Learning for Email Machine Learning for Email
by Drew Conway, John Myles White
October 2011
Print: $24.99
Ebook: $20.99

Webcast: Machine Learning for Hackers Author Talks Bandit Algorithms for The Web
February 05, 2013
In this webcast presented by John Myles White, author of Bandit Algorithms for Website Optimization, Machine Learning for Hackers, and Machine Learning for Email, we'll describe how to build better websites by using a family of algorithms called Bandit...

Webcast: An Introduction to Machine Learning for Hackers
September 18, 2012
We'll introduce programmers to two of the most common tools in the machine learning toolkit: linear regression and logistic regression.

"If you’re looking for a very solid, well written, overview of machine learning in R, Machine Learning for Hackers is a great starting point, as long as you’re willing to read around the subject (the recommended texts and books cited list works well)."
--Jonathan Hammler, Compsoc -- Durham University Computing Society

"I recommend this book to anyone with interest in machine learning with a statistics background."
--Nilesh Thatte, BCS

"If you’re looking for a hands-on introduction to machine learning, maybe as a prelude to or complement to a more theoretical text, you’ll enjoy this book."
--John D. Cook, The Endeavour

"I recommend it to any programmer who needs to generate predictions or classifications from data -- using R and learning more about the statistical techniques behind the methods will help you to create better data hacking applications in the long run."
--David Smith, Revolution Analytics