Interpretable Machine Learning Use-Cases - Kaggle 30 Days of ML Day 15

Explore interpretable machine learning and its practical applications in this comprehensive video tutorial. Dive into the world of machine learning explainability and explainable AI, focusing on the importance of model insights. Learn about blackbox models, feature importance, and the reasons behind the need for ML interpretability. Discover how model insights can be used for debugging, feature engineering, mathematical understanding, future data predictions, building business cases, fostering trust, and mitigating legal risks. Follow along with the Kaggle 30 Days of ML Challenge content, suitable for both registered Kagglers and unregistered machine learning enthusiasts. Gain valuable knowledge on making machine learning models more transparent and understandable, enhancing your skills in this crucial aspect of AI development.

Introduction
Blackbox Models
Feature Importance
Why ML Interpretability
Debugging
Feature Engineering
Mathematics
Future Data
Business Case
Building Trust
Legal Risk