What this quest delivers
By the end of the next seven tracks you will be able to take a tabular problem from first conversation to deployed artifact: write the contract, audit columns, build a leakage-safe pipeline, train a sane baseline, choose between linear models and tree ensembles, calibrate probabilities, pick an honest threshold, monitor for drift, and document the handoff.
Habits before tools
Each track teaches a small set of tools but the bigger lift is the habit: the column audit before features, the baseline before fancy models, the calibration before threshold, the rollback before launch. Tools change every two years; these habits do not.
How to study
Bring a real or realistic dataset. Apply each lesson to it as you go. The exercises are written assuming you have a project; without one, the lessons compress into trivia. The companion data-engineering quest covers the ingestion side; this quest assumes a sane DataFrame is in front of you.
kaggle project 도전 해 보겠습니다. 이래서 족장님이 kaggle 해보라고 하셨던 거구나.. 많이 늦었지만 간단한걸로 시작해 보겠습니다.