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Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
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 FeaturesExplore various explainability methods for designing robust and scalable explainable ML systemsUse XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problemsDesign user-centric explainable ML systems using guidelines provided for industrial applicationsBook DescriptionExplainable 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 learnExplore various explanation methods and their evaluation criteriaLearn model explanation methods for structured and unstructured dataApply data-centric XAI for practical problem-solvingHands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and othersDiscover industrial best practices for explainable ML systemsUse user-centric XAI to bring AI closer to non-technical end usersAddress open challenges in XAI using the recommended guidelinesWho this book is forThis 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 ContentsFoundational Concepts of Explainability TechniquesModel Explainability MethodsData-Centric ApproachesLIME for Model InterpretabilityPractical Exposure to Using LIME in MLModel Interpretability Using SHAPPractical Exposure to Using SHAP in MLHuman-Friendly Explanations with TCAVOther Popular XAI FrameworksXAI Industry Best PracticesEnd User-Centered Artificial Intelligence
Key FeaturesExplore various explainability methods for designing robust and scalable explainable ML systemsUse XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problemsDesign user-centric explainable ML systems using guidelines provided for industrial applicationsBook DescriptionExplainable 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 learnExplore various explanation methods and their evaluation criteriaLearn model explanation methods for structured and unstructured dataApply data-centric XAI for practical problem-solvingHands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and othersDiscover industrial best practices for explainable ML systemsUse user-centric XAI to bring AI closer to non-technical end usersAddress open challenges in XAI using the recommended guidelinesWho this book is forThis 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 ContentsFoundational Concepts of Explainability TechniquesModel Explainability MethodsData-Centric ApproachesLIME for Model InterpretabilityPractical Exposure to Using LIME in MLModel Interpretability Using SHAPPractical Exposure to Using SHAP in MLHuman-Friendly Explanations with TCAVOther Popular XAI FrameworksXAI Industry Best PracticesEnd User-Centered Artificial Intelligence
304 pages, Kindle Edition
Published July 29, 2022
1 person is currently reading
11 people want to read
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Displaying 1 - 3 of 3 reviews
October 26, 2022
I think this is one of the best books on Explainable AI! This is the fifth book that I am reading on this topic. Other XAI books are extremely theoretical. They don't guide you how to apply the explainability techniques in practice. Whereas, this book covers every practical step from setup of Python framework till detailed explanation and inference of the output of the explainability methods. Kudos to the author for writing such a wonderful book with so many practical examples! It is highly recommended from my side! Especially the code tutorials provided at: https://github.com/PacktPublishing/Ap...
September 9, 2022
good book overall if you want to quickly grasp what each technique is about and why they are useful.
I would give 5 start though if the explanation of techniques can be a bit more in details. For example, the explanation of Anchor and Counterfactual Explanations seem quite brief
I would give 5 start though if the explanation of techniques can be a bit more in details. For example, the explanation of Anchor and Counterfactual Explanations seem quite brief
October 26, 2022
Loved the book! Data Centric Explanations by Aditya is the next big thing in Explainable AI! Highly recommended book!
Displaying 1 - 3 of 3 reviews