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image for course Explainable Machine Learning

Explainable Machine Learning

Learn how to interpret decisions made by simple, inherently interpretable machine learning models. Explore techniques for calculating and visualizing feature importance, and use these insights to explain overall model behavior and individual predictions.

Course modules

Introduction

Familiarize yourself with the course and the Regression Learner app.

Lessons:
  • Course Overview
  • Course Example: Predict Daily Bike Rentals

Interpretable Models

Train and interpret decision tree and linear regression models.

Lessons:
  • Overview of Interpretable Models
  • Decision Tree Model Interpretation
  • Linear Regression Model Interpretation

Global Explanations

Apply global explanation methods to understand model behavior.

Lessons:
  • Overview of Global Explanations
  • Global Importance
  • Global Summary
  • Global Dependence

Local Explanations

Apply local explanation methods to understand individual predictions behavior.

Lessons:
  • Overview of Local Explanations
  • Local Shapley
  • LIME (Local Interpretable Model-Agnostic Explanations)
  • Local Explanations in Practice

Conclusion

Learn next steps and give feedback on the course.

Lessons:
  • Summary
  • Additional Resources
  • Survey

Format:Self-paced

Language:English

Language

  • Hands-on exercises with automated feedback
  • Access to MATLAB through your web browser
  • Shareable progress report and course certificate

Classification Methods with Machine Learning

Learn and apply different machine learning methods for classification.

Regression Methods with Machine Learning

Learn and apply different machine learning methods for regression.

Machine Learning Onramp

Learn the basics of practical machine learning methods for classification problems.

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