Simplified Way to Learn XAI
Sub Category
- Data Science
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Objectives
- Importance of XAI in modern world
- Differentiation of glass box, white box and black box ML models
- Categorization of XAI on the basis of their scope, agnosticity, data types and explanation techniques
- Trade-off between accuracy and interpretability
- Application of InterpretML package from Microsoft to generate explanations of ML models
- Need of counterfactual and contrastive explanations
- Working principles and mathematical modeling of XAI techniques like LIME, SHAP, DiCE, LRP, counterfactual and contrastive explanationss
- Application of XAI techniques like LIME, SHAP, DiCE, LRP to generate explanations for black-box models for tabular, textual, and image datasets.
- What-if tool from Google to analyze data points and to generate counterfactuals
Pre Requisites
- No programming experience needed. You will learn everything you need to know to apply XAI for generating explanations for ML models.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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