Applied Machine Learning Explainability Techniques

Free Download Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more by Aditya Bhattacharya
English | July 29, 2022 | ISBN: 1803246154 | 306 pages | MOBI | 13 Mb
Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems

Key Features
Explore various explainability methods for designing robust and scalable explainable ML systems
Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
Design user-centric explainable ML systems using guidelines provided for industrial applications
Book Description
Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.
Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You’ll begin by gaining a conceptual understanding of XAI and why it’s so important in AI. Next, you’ll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you’ll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.
By the end of this ML book, you’ll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.
What you will learn
Explore various explanation methods and their evaluation criteria
Learn model explanation methods for structured and unstructured data
Apply data-centric XAI for practical problem-solving
Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
Discover industrial best practices for explainable ML systems
Use user-centric XAI to bring AI closer to non-technical end users
Address open challenges in XAI using the recommended guidelines
Who this book is 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.
Table of Contents
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

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