Deep Learning Fundamentals

Model, loss, and optimizer: the core components of deep learning. Come learn more; at the end, we even build the beginnings of a deep learning framework!

Bayesian Data Science Two Ways: Simulation and Probabilistic Programming

Bayesian statistical analysis concepts, taught using PyMC3, with Hugo Bowne-Anderson.

Network Analysis Made Simple

My take on teaching network analysis and graph theory concepts, using NetworkX. Taught at many conferences since 2015.

Network Analysis on DataCamp

I created the network analysis curriculum on DataCamp with Hugo Bowne-Anderson.

Data Testing Tutorial

In this tutorial, we show data scientists how to write tests. Idea hatched with Renee Chu.

scikit-learn Tutorial

One of my earliest tutorials, on how to use scikit-learn. Delivered in 2015.