In this comprehensive guide, the author aims to provide practical insights into artificial intelligence (AI) model explainability and interpretability using Python frameworks. While Mishra doesn’t explicitly state their intention, it is evident that he wants to equip readers with the knowledge and tools to make AI algorithms more understandable, as well as to address bias, ethics, and reliability.

The book covers a wide range of topics, starting with the basics of model explainability and ethical considerations. It then delves into methods for interpreting linear, nonlinear, and time-series models, as well as ensemble models and natural language processing (NLP) tasks. The author introduces Python libraries like LIME, SHAP, Skater, and ELI5 to enhance interpretability. The book also explores deep learning models, rule-based expert systems, and computer vision tasks using various explainable AI (XAI) frameworks.

Readers should have a basic understanding of AI concepts, but the book is designed for AI engineers, data scientists, and software developers involved in AI projects. By the end of the book, readers will have gained a solid understanding of AI model explainability and the ability to make AI models interpretable and explainable. They will also learn how to assess fairness, quantify reliability, and build trust in AI models.

Practical explainable AI using Python combines textbook and cookbook elements. It provides explanations of concepts along with practical examples and exercises. While no specific accessories are mentioned, readers can likely find additional resources and code examples online. While the field of XAI is rapidly evolving, this book offers a comprehensive foundation that will remain relevant for some time. However, readers should supplement their knowledge with the latest research in order to stay up to date in this dynamic field.