Interpretable Machine Learning with Python
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About
Interpretable Machine Learning with Python offers a comprehensive introduction to both white-box models like linear regression and decision trees, and the fundamentals of interpretability and explainability. It delves into model-agnostic methods such as SHAP, anchors, and counterfactuals, which make complex machine-learning models, including deep learning for vision and text, understandable and accountable. The book also covers advanced techniques for causal inference and uncertainty. It guides readers to become "machine learning model mechanics" by leveraging techniques like bias mitigation, feature selection, and adversarial robustness. Intended for data scientists, machine learning engineers, MLOps engineers, and those interested in responsible AI development, it requires a solid foundation in Python and serves as a bridge to understanding the relationship between AI and the real world, promoting ethical technology development.
Platform
Features
• focuses on responsible and ethical ai development
• addresses adversarial robustness
• provides methods for bias mitigation and feature selection
• includes advanced techniques for causal inference
• covers deep learning interpretation for vision and text
• details model-agnostic methods: shap, anchors, counterfactuals
• explains white-box and black-box models
• comprehensive guide to machine learning interpretability
FAQs
Who is the primary audience for this book?
The book is intended for data scientists, machine learning engineers, MLOps engineers, and anyone interested in responsible AI development.
What are the core topics covered in the book?
It covers white-box and black-box models, model-agnostic methods like SHAP, deep learning interpretation, causal inference, bias mitigation, and adversarial robustness.
Is a background in Python necessary to use this book?
Yes, a solid foundation in Python is recommended to fully grasp the concepts and apply the practical examples within the book.
Pricing Plans
Book Purchase
Unknown Price• Comprehensive introduction to interpretability
• Covers white-box and black-box models
• Model-agnostic methods like SHAP, anchors, counterfactuals
• Deep learning interpretability for vision and text
• Advanced techniques for causal inference
• Bias mitigation strategies
• Feature selection methods
• Adversarial robustness guidance
• Promotes ethical AI development
• Practical Python examples
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