Advanced ML & Model Interpretability
Master model explanation techniques (SHAP, LIME), advanced evaluation metrics, hyperparameter tuning, and real-world deployment patterns.
Start ModuleWelcome to Advanced ML π§
You've mastered fundamentals. Now build production-ready models.
The difference between a decent model and an industry-grade one:
- Interpretability - Explain why your model made a prediction
- Advanced algorithms - XGBoost, SHAP, LIME, stacking
- Production patterns - Monitoring, deployment, edge cases
- Real-world challenges - Class imbalance, data drift, ethical AI
Key Skills
β SHAP & LIME - Explain any prediction β Hyperparameter tuning - Optimize for maximum accuracy β Handle imbalanced data - When you have 99% negatives β Deploy models safely - Version control, monitoring, rollback β Detect model drift - When performance degrades
Prerequisites
β Module 5 (ML Fundamentals - all algorithms)
Let's build enterprise-grade ML! π
Curriculum
Advanced Evaluation Metrics
Go beyond accuracy with AUC-ROC, Precision-Recall curves, RMSE, MAE, and when to use each.
Stratified K-Fold Cross-Validation
Ensure class distribution is consistent across folds using Stratified splits.
SHAP (SHapley Additive exPlanations)
Explain any model's predictions using game theory. Understand why the model made that decision.
LIME (Local Interpretable Model-agnostic Explanations)
Approximate any black-box model with simple local decision rules.