An introduction to probabilistic thinking — and why it’s the foundation for robust and explainable AI systems.

An accessible dive into correlation, base proportions, summary statistics, and uncertainty.

Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log…

The subjective details holding together one of economics’ favourite models

A theoretical and practical introduction to non-parametric density estimation.

A generalized version of Kaplan-Meier allows to model a continuous value (like money) instead of a binary signal (like survival)

Mastering the fundamentals of bagging and boosting with simple examples

A journey into three intuitions: Common, Bayesian and Causal

A breakdown of the three fundamental math fields required for machine learning: statistics, linear algebra, and calculus.

Using Python to determine where NBA coaches come from and what makes them successful