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Project Deep Dives

These pages bring project-level details directly into the docs site so students can see concrete commands, outputs, and interpretation patterns without leaving MkDocs.

Included Deep Dives

Deep Dive Focus Time Primary Artifacts
Supervised Learning classification/regression foundations, imbalance handling, model evaluation 90-150 min metrics tables, curves, learning diagnostics
Causal Inference ATE/CATE/tau(x), uplift modeling, targeting policies 120-180 min Qini curves, uplift-at-k, policy simulations
MLOps Drift training, drift detection, retrain decisions, local serving 90-150 min drift report, policy decision JSON, API outputs
Ranking Track grouped ranking modeling + API productization workflow 90-150 min group split manifests, NDCG metrics, ranking API outputs
Forecasting Track time-aware forecasting + observability-ready API workflow 90-150 min time split manifests, forecast metrics, demand API metrics

How To Use These Pages

  1. Pick one deep dive and run the quickstart exactly once.
  2. Validate artifact generation.
  3. Use the "How to interpret" checklist to turn outputs into decisions.
  4. Move to the next deep dive only after you can explain current outputs in plain language.

Source Project Documents