Interpretable Machine Learning
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About
Interpretable Machine Learning is designed to educate users about making AI transparent, fair, and accountable. The tool emphasizes the use of interpretable machine learning techniques through Python and includes the latest methods for model-agnostic approaches, causal inference, and practical insights into machine learning deployment. The tool also highlights the importance of responsible AI practices, enhancing understanding and equitable outcomes for users and communities.
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Task
Features
• focus on ethical ai practices
• understand and interpret machine learning models
• practical applications with python
• model-agnostic methods for explainability
• training on causality and uncertainty in ai
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