Model Explainability — SHAP & LIME for Interpretable AI
Machine Learning skills for the Data Scientist career path. Filter, model, and reason about the real data this role works with every day.
Machine Learning skills for the Data Scientist
Building on XGBoost & Gradient Boosting, this lesson focuses on the practical Machine Learning toolkit a working Data Scientist reaches for on the job.
Try the first query
SELECT example, value
FROM machine-learning_dataset
WHERE priority = '2'
AND career = 'Data Scientist';
▸ preview · Sprint-2 will replace this with a live runner
Data Visualization for Insights
Once this topic clicks, the recommended next step keeps the learning streak going on adjacent skills in the same career path.
continue path →Sprint-1 stub
This is the Sprint-1 stub for ai.careerplane.data-analytics.data-scientist.machine-learning.explainability.
The fully-generated topic page (sandbox + quiz + AI hints + interactive
tooling) lands in Sprint-2 at
(per arch/memo.md → D-2026-04-22-13). The
//go:embed all:static directive auto-serves
it on the next container rebuild — no server-code change needed.