Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
Author:   Aditya Bhattacharya
Publisher:   Packt Publishing Limited
ISBN:  

9781803246154


Pages:   304
Publication Date:   29 April 2022
Format:   Paperback
Availability:   Available To Order   Availability explained
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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more


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Author:   Aditya Bhattacharya
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781803246154


ISBN 10:   1803246154
Pages:   304
Publication Date:   29 April 2022
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

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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Author Information

Aditya Bhattacharya is an Explainable AI Researcher at KU Leuven with the mission to bring AI closer to end-users. Previously, I had worked as the AI Lead and a data scientist at West Pharmaceuticals. I have an overall exposure of 6 years in Data Science, Machine Learning, IoT, and Software Development. I have led more than 20 AI projects and programs democratizing AI practice for West and Microsoft. In West, I have contributed to forming the AI team and developed end-to-end solutions from scratch. I also have people management experience of about 2 years at West and have led and managed a global team of 10+ members.

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