Applied Machine Learning Explainability Techniques

<p><strong>Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems</strong></p><p><br></p><p><strong>Key Features:</strong></p><ul><li>Explore various explainability methods for designing robust and scalable explainable ML systems</li><li>Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems</li><li>Design user-centric explainable ML systems using guidelines provided for industrial applications</li></ul><p><br></p><p><strong>Book Description:</strong></p><p>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.</p><p>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.</p><p>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.</p><p><br></p><p><strong>What You Will Learn:</strong></p><ul><li>Explore various explanation methods and their evaluation criteria</li><li>Learn model explanation methods for structured and unstructured data</li><li>Apply data-centric XAI for practical problem-solving</li><li>Hands-on exposure to LIME SHAP TCAV DALEX ALIBI DiCE and others</li><li>Discover industrial best practices for explainable ML systems</li><li>Use user-centric XAI to bring AI closer to non-technical end users</li><li>Address open challenges in XAI using the recommended guidelines</li></ul><p><br></p><p><strong>Who this book is for:</strong></p><p>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.</p>