Cross-validation and hyperparameter tuning

All lessons in this track

1. 1
   What machine learning is, and when not to use it ~25 min
2. 2
   The split, and the leakage that ruins it ~30 min
3. 3
   Regression: predicting a number ~30 min
4. 4
   Regression metrics: MAE, RMSE and R² ~25 min account needed
5. 5
   Classification: predicting a category ~30 min account needed
6. 6
   Classification metrics: beyond accuracy ~30 min account needed
7. 7
   Feature engineering: encoding and scaling ~30 min account needed
8. 8
   Pipelines: the professional way to build a model ~30 min account needed
9. 9
   Cross-validation and hyperparameter tuning ~30 min account needed
10. 10
    Trees, forests and gradient boosting ~30 min account needed
11. 11
    Clustering and dimensionality reduction ~30 min account needed
12. 12
    Overfitting, diagnosis and the final report ~30 min account needed