دانلود کتاب 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 systemsKey Features  Explore 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 learn  Explore 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 designed for scientists, researchers, engineers, architects, and managers who are actively engaged in the field of Machine Learning and related areas. In general, anyone who is interested in problem-solving using AI would be benefited from this book. The readers are recommended to have a foundational knowledge of Python, Machine Learning, Deep Learning, and Data Science. This book is ideal for readers who are working in the following roles:  Data and AI ScientistsAI/ML EngineersAI/ML Product ManagersAI Product OwnersAI/ML ResearchersUser experience and HCI ResearchersTable of Contents Foundational 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