AILAB Track 2: Classical ML & Scikit-Learn Pipeline Mastery

01 / Why a student should open this

End-to-end practical guide to feature encoding, cross-validation, hyperparameter tuning, and decision trees/ensembles before touching neural nets.

02 / Student Context & Field Notes

Best for

Tabular data modeling & AILAB take-home test

Time commitment

Weekend dive

Prerequisites

Pandas dataframes and basic Python OOP.

Best part / timestamp

Pipeline and ColumnTransformer section eliminates data leakage.

Watch out / Caveat

Always split train/test before fitting scalers or encoders.

Personal note from contributor

“AILAB recruiters heavily check whether your validation strategy has target leakage.”

https://scikit-learn.org/stable/tutorial/index.html