Interpretable Machine Learning - Kaggle Course

Learn machine learning explainability techniques through this comprehensive 5-video tutorial series covering essential interpretability methods. Explore real-world use cases for interpretable machine learning and discover when model explainability becomes crucial for decision-making. Master feature importance analysis using the ELI5 library to understand which variables drive your model's predictions. Dive into Partial Dependence Plots to visualize how individual features affect model outcomes across different value ranges. Understand SHAP (Shapley Additive Explanations) values and their mathematical foundation for explaining individual predictions. Analyze SHAP summary plots to gain insights into feature contributions and model behavior patterns. Each video combines theoretical concepts with practical Python implementations, providing hands-on experience with popular explainability libraries and techniques used in production machine learning systems.

Syllabus

Kaggle 30 Days of ML (Day 15) - Interpretable Machine Learning Use-cases
Kaggle 30 Days of ML (Day 16) - Feature Importance of Machine Learning with ELI5
Kaggle 30 Days of ML (Day 17) - Partial Dependence Plot - Interpretable Machine Learning - XAI
Kaggle 30 Days of ML (Day 18) - SHAP - Shapley Values - Interpretable Machine Learning - XAI
Kaggle 30 Days of ML (Day 19) - Understanding SHAP Summary Plot - Interpretable Machine Learning