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3
Live ML Cases
Six end-to-end walkthroughs: churn prediction pipeline, diagnosing data leakage, tuning XGBoost for fraud, picking metrics for business decisions, customer segmentation via clustering, and a light MLOps deployment.
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145 min total
2 Lessons
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Module Content
SUBMISSION: Capstone 1 — Model Interpretability & Bias Audit with SHAP Values
Explain a credit-approval XGBoost with SHAP, quantify a segment-level disparity, mitigate it, and ship a model card a risk team can sign.
45 minSubmission
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SUBMISSION: Capstone 2 — Baseline-to-Boosted Model Comparison & Validation Suite
Run a fraud-model bake-off from dummy to LightGBM under one honest validation suite (PR-AUC, Brier, latency), crown a champion, and leave a drift-monitoring plan.
45 minSubmission
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