If you only have time for one resource, make it StatQuest. Run by Josh Starmer, PhD—a former genetics researcher who started making videos to explain stats to his colleagues and discovered a gift for teaching—this YouTube channel has grown to over 1.5 million subscribers for a reason. It covers statistics, machine learning, neural networks, and AI, from basic concepts like p-values and linear regression all the way through advanced topics like XGBoost and transformers. (You can confirm all of this on his official site, statquest.org, which is very much alive and kicking.)
Before we build a black box and then struggle to explain it, Molnar makes a radical suggestion: what if we just… didn’t? What if we used a model that’s already transparent? This isn’t a beginner’s compromise.
Let’s start with a single decision tree. A deep tree memorizes every detail of the training data – including the noise. That’s **high variance**: change the training set slightly, and the tree’s predictions change a lot.
Underwriting is the insurance industry's oldest classification problem. A gatekeeper — historically a person with a rulebook — decides: *Should we insure this person?* And if yes, *how much should they pay?*
Let's start with the simplest classical controller: PID. The name stands for Proportional-Integral-Derivative, but you don't need to remember that. Here's what it actually does:
Learn how to translate black-box model decisions into stakeholder-ready explanations using SHAP force plots and summary plots, building trust without complex math.
A side-by-side reference applying PDP, ICE, permutation importance, LIME, and SHAP to the same model so you can see what each tells you and where they diverge.
Learn to use SHAP values to explain individual model predictions, see which features drove each decision, and uncover hidden bias using Python and force plots.
Learn how LIME's fast local perturbations and SHAP's game-theoretic Shapley values explain black-box model predictions, and when to use each for your projects.