Advanced · Intensive
Feature Engineering Studio
Practical feature pipelines: encoding, scaling, leakage prevention, and validation splits. Includes pair programming sessions with instructors.
Request informationFeatures
- Pipeline design with sklearn ColumnTransformer
- Target leakage detection exercises
- Feature store concepts (conceptual, not vendor-specific)
- Imbalanced class handling techniques
- Documentation templates for feature catalogs
- Peer review of two pipeline designs
Outcomes
- Ship a reproducible sklearn Pipeline artifact
- Document feature definitions for team handoff
- Diagnose leakage in a provided broken notebook
Arjun Patel
ML engineer; former feature platform team lead.
FAQ
Completion of ML Foundations or equivalent sklearn experience.
Reviews
"Leakage hunt on day 3 was brutal — exactly what I needed before our next release."