Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

"Most machine learning approaches are black boxes. The days of learning how they work out of curiosity are over; explaining how ML predictions are made is now becoming legally mandatory. This book details effective ways to achieve explainability. A must-have resource for all ML practitioners, from beginners to experts."

Dr. Yogesh Kulkarni, Principal Architect, CTO office, Icertis

"Explainability is going to play a big role in the usability of models in a range of important applications. The author takes the very organized approach of starting with the basics, discussing common libraries such as SHAP and LIME, and then expanding to a broader horizon with other XAI frameworks. The concepts have been presented clearly and are accompanied by real-world examples and intuitive diagrams, making it a great read. The code excerpts are succinct and adequate. This will be really helpful for readers who are getting started in XAI."

Saptarshi Goswami, Digital Transformation Lead, IT & Innovation Cell, at Department of Higher Education, Govt. of WB

"This book lucidly demonstrates the concepts and algorithms related to image generation, along with providing executable code. This is the perfect book for machine learning researchers and engineers to build a strong foundation in this domain."

Sabyasachi Mukhopadhyay, Research Scholar, Center for Computational Data Science, IIT Kharagpur, Google Developer Expert in Machine Learning

"Explainable AI is a multi-disciplinary perspective that combines learnings from the fields of science, technology, and research with the mental model of users. The author of this book has done a fantastic job in portraying this multi-disciplinary perspective through detailed explanations of the concepts, interesting examples, graphical representations, and step- by- step code examples that anyone with foundational knowledge of Python can follow. The book provides knowledge about XAI with a unique blend of recommendations from industrial and academic research practices. I highly appreciate the efforts taken by the author to explain every topic with sufficient details that can be followed by beginners to experts of machine learning. This is highly recommended for all ML practitioners!"

Shreya Bhattacharya -- solar physics researcher at Royal Observatory of Belgium