13 Monday evenings taking you from your first churn model to gradient boosting, forecasting, and LLM-powered features.
What predictive analytics is, why every business question about the future is a prediction problem — and a churn model you build yourself before 7:30
Real data arrives in eight tables, three date formats, and a thousand blanks. pandas joins, groupbys, and datetime features on Olist e-commerce — and how to spot the leak that makes a model look brilliant and ship useless
Encoders, scalers, imputers, and the scikit-learn Pipeline — the machinery that turns a messy table into a model input without ever peeking at the test set
Logistic regression as odds, kNN and Naive Bayes as contrasts, the confusion matrix in depth, precision vs. recall, ROC vs. PR — on 30,000 credit-card customers, 22% of whom default
A model produces a probability; a business produces a decision. Expected value, profit curves, tuned thresholds, calibration, lift and gain — and the launch of the Midterm Model Competition
What is a house worth? Linear regression as the interpretable baseline, coefficients in dollars, log targets, ridge and lasso, and the residual plot that tells you what the model is missing
How a tree asks questions, why it memorizes, and how averaging hundreds of decorrelated trees turns a fragile model into the most reliable workhorse in tabular ML
Fit the residuals, again and again: HistGradientBoosting, LightGBM, XGBoost, early stopping — and the tabular foundation models that, since 2025, beat tuned boosting on small data with zero tuning. Plus: the competition reveal
Cross-validation that doesn't lie, hyperparameter search with Optuna, SHAP for one customer and the whole book, conformal intervals that keep their promise, fairness slices, and the one-page model card
Learning without labels: RFM customer segmentation with k-means, choosing k honestly, hierarchical clustering and DBSCAN, PCA and UMAP for seeing high-dimensional data, and Isolation Forest for the transactions that don't belong
Why random splits lie when time matters, lag and rolling features that turn forecasting into regression, classical baselines you must beat, and the zero-shot foundation models that changed the field in 2025
A multilayer perceptron on tabular data (and why it usually loses to boosting), text as features — TF-IDF, sentence embeddings, zero-shot LLM labels — and the AI-assisted analyst's workflow: generate, verify, own
Persist the pipeline, track it, put a front end on it, watch it drift, know the rules — then the night the course was built for: your prediction stories, presented