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18
Projects
Machine-Learning Applications & Projects
Putting it all together: building, comparing, explaining and presenting a real ML project.
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What you’ll take away
- A project is a pipeline: problem → data → model → evaluation → deployment → monitoring.
- Compare models fairly: same splits, same tuning effort, always a baseline.
- Interpretation tools (permutation importance, SHAP) build trust and find bugs.
- Present as a story: problem, data, approach, results, limitations.
Major concepts (syllabus)
End-to-end ML pipelines, model comparison, interpretation, project presentations
Lessons
- End-to-end ML pipelinesFrom raw data to a deployed, monitored model.10 min
- Model comparisonFair baselines, cross-validation and statistical comparison.9 min
- InterpretationFeature importance, partial dependence and explaining predictions.9 min
- Project presentationsTelling the story of your problem, method and results.7 min
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