Explore the concept of Interpretable Machine Learning, also known as Machine Learning Explainability and Explainable AI, in this comprehensive video tutorial. Delve into the importance and relevance of machine learning explainability, discover various types of interpretable machine learning techniques, and gain hands-on experience with Python examples. Learn about LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), including their advantages and disadvantages, through practical demonstrations. Enhance your understanding of how to make machine learning models more transparent and interpretable, equipping yourself with valuable skills for ethical and responsible AI development.
What is Interpretable Machine Learning - ML Explainability - with Python LIME Shap Tutorial
1littlecoder via YouTube
Overview
Syllabus
Introduction - Outline
Credits
What is Interpretable Machine Learning?
Why is Machine Learning Explainability Required?
How is IML relevant to me?
Types of IML
LIME , Advantages and Disadvantages of LIME with Python Tutorial
SHAP , Advantages and Disadvantages of SHAP
Taught by
1littlecoder
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