G4PythonAdvancedAbout 210 min to complete
End-to-End Machine Learning Workflow (Scikit-learn)
This is the finale of the numbered curriculum — the notebook that ties everything together. You've learned the pieces: preparing data (47), classifying (48), clustering (49).
Notebook
About 210 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
This is the finale of the numbered curriculum — the notebook that ties everything together. You've learned the pieces: preparing data (47), classifying (48), clustering (49).
By the end of it
- Execute the full ML lifecycle: frame → prepare → select → tune → evaluate → interpret → persist
- Build one leak-free Pipeline combining preprocessing (ColumnTransformer) and a model
- Compare candidate models with cross-validation, then tune the winner with GridSearchCV / RandomizedSearchCV
- Diagnose fit with learning curves and validation curves
- Perform the final, single evaluation on held-out data and interpret it (metrics, confusion matrix, feature importance)
- Save and reload a trained pipeline with joblib for reuse/deployment
- Structure a project so that data leakage is impossible from start to finish