Applied Machine Learning Explainability Techniques
Explainable AI is a set of techniques used to demystify the outcome of machine learning and AI models, making algorithms more trustworthy and transparent by justifying model predictions. This book helps you to learn how to design explainable ML systems for industrial applications considering the best practices.
Offered by
Difficulty Level
Intermediate
Completion Time
10h12m
Language
English
About Book
Who Is This Book For?
This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.
Applied Machine Learning Explainability Techniques
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 10h12m total length
Foundational Concepts of Explainability Techniques
Model Explainability Methods
Data-Centric Approaches
LIME for Model Interpretability
Practical Exposure to Using LIME in ML
Model Interpretability Using SHAP
Practical Exposure to Using SHAP in ML
Human-Friendly Explanations with TCAV
Other Popular XAI Frameworks
XAI Industry Best Practices
End User-Centered Artificial Intelligence
Related Resources
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