Applied Machine Learning Explainability Techniques
Aditya Bhattacharya
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Add to basketSold by PBShop.store US, Wood Dale, IL, U.S.A.
AbeBooks Seller since 7 April 2005
Condition: New
Quantity: Over 20 available
Add to basketNew Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
Seller Inventory # L0-9781803246154
Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems
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.
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.
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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