-
Course
- AI
Explainability Methods for Black-box AI Models
Explaining black-box AI models is an increasingly critical skill in today's AI landscape. This course will teach you practical explainability techniques like LIME and SHAP to understand, debug, and communicate how your complex models make decisions.
What you'll learn
Complex AI models often function as "black boxes," creating challenges for debugging, stakeholder communication, and ethical deployment. In this course, Explainability Methods for Black-box AI Models, you'll learn to implement techniques to understand how your models make decisions. First, you'll explore the need for explainability and the landscape of explanation methods. Next, you'll discover how to implement LIME and SHAP with Python examples. Finally, you'll learn how to avoid common pitfalls and apply best practices when integrating explainability into real-world AI projects. When you're finished, you'll have a fundamental understanding of explainability techniques to make your models more transparent, trustworthy, and easier to communicate to stakeholders.
Table of contents
About the author
Doru founded and runs a marketing agency with global clients in a wide range of industries. His focus is on campaigns that convert and get new business through the door, not feel good campaigns. In terms of approach he believes in authentic and blunt advertising without the presence of small print.
More Courses by Doru