{"product_id":"applied-machine-learning-explainability-techniques-make-ml-models-explainable-and-trustworthy-for-practical-applications-using-lime-shap-and-more-paperback","title":"Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more - Paperback","description":"\u003cp\u003eby \u003cb\u003eAditya Bhattacharya\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eLeverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore various explainability methods for designing robust and scalable explainable ML systems\u003c\/li\u003e\n\u003cli\u003eUse XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems\u003c\/li\u003e\n\u003cli\u003eDesign user-centric explainable ML systems using guidelines provided for industrial applications\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eExplainable 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.\u003c\/p\u003e\u003cp\u003eApplied 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.\u003c\/p\u003e\u003cp\u003eBy 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.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eExplore various explanation methods and their evaluation criteria\u003c\/li\u003e\n\u003cli\u003eLearn model explanation methods for structured and unstructured data\u003c\/li\u003e\n\u003cli\u003eApply data-centric XAI for practical problem-solving\u003c\/li\u003e\n\u003cli\u003eHands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others\u003c\/li\u003e\n\u003cli\u003eDiscover industrial best practices for explainable ML systems\u003c\/li\u003e\n\u003cli\u003eUse user-centric XAI to bring AI closer to non-technical end users\u003c\/li\u003e\n\u003cli\u003eAddress open challenges in XAI using the recommended guidelines\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 306\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.64 x 9.25 x 7.5 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e July 29, 2022\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":42718701748287,"sku":"9781803246154","price":81.19,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0105\/8226\/1823\/files\/cb22c4216cdf15cbd31363d9345dc213.webp?v=1765082936","url":"https:\/\/dhlswag.com\/products\/applied-machine-learning-explainability-techniques-make-ml-models-explainable-and-trustworthy-for-practical-applications-using-lime-shap-and-more-paperback","provider":"BBB","version":"1.0","type":"link"}