Explainability Methods for Black-box AI Models
Explainability Methods for Black-box AI Models
Released 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 36m | Size: 85 MB
Released 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 36m | Size: 85 MB
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.
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.