What You'll Learn

• Identify limitations of traditional interpretation methods: Explore why Partial Dependence Plots (PDPs) can mislead when features are correlated, creating unrealistic scenarios and impossible feature combinations that don't reflect actual data patterns.
• Implement ALE plots from scratch: Learn the theory and implementation of Accumulated Local Effects plots, learning how they isolate individual feature effects while respecting realistic data combinations and handling correlated features properly.
• Generate actionable business insights: Translate complex model behavior into concrete business recommendations for fleet management, operational timing, and revenue optimization strategies.
  
  

Who Should Enroll

• Data Scientists and ML Engineers working with black-box models who need to explain predictions to stakeholders, ensure model fairness, or debug unexpected model behavior. This project provides essential skills for making complex models interpretable and trustworthy in production environments.
• Business Analysts and Consultants who use machine learning models to drive strategic decisions and need to understand not just what the model predicts, but why it makes those predictions. The business insight generation techniques taught here are directly applicable to any domain where ML drives decision-making.
• Students and Researchers in data science, statistics, or related fields who want to understand advanced model interpretation techniques beyond basic feature importance. This project covers cutting-edge explainable AI methods that are increasingly important in academic research and industry applications.
  
  

Why Enroll

What You'll Need