I am a contributor to PyMC3, a “Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms.”
An essay on building linear regression models. It is converted from notes for a talk I gave at Rice University in September 2014. Contains lots of pictures, lots of interactivity, and a modest amount of math. As a bonus, everything is typeset with KaTeX.
A demonstration of linear regression, overfitting, normalization, and regularization. Allows you to choose data from a distribution, and interactively fit polynomials to the data using least squares, ridge, or Lasso regression.
A demonstration of using Flask, AngularJS, and d3js to visualize machine learning models. This is meant to be a minimal example of how to put together such a demo, showing how to make the tech stack play nice.