How can you effectively explain Al's decision to A's business and stakeholders? You need careful planning, design, and visualization. The problems to be addressed, the models, and the relationships between variables are often subtle, unexpected, and complex.
"Python Explainable AI (XAI) in Practice" allows you to master many XAI tools and methods in practice through several well-designed projects, and the methods only stay in theory and concept. You'll build models hands-on, interpret results visually, and integrate XAI tools. You'll build XAI solutions using Python, TensorFlow 2, Google Cloud XAI Platform, Google Colaboratory, and other frameworks to open the black box of machine learning models. This book covers several open-source XAI tools in Python that can be used throughout the machine learning project lifecycle. You'll learn how to explore machine learning model results, examine key influencing variables and variable relationships, detect and address bias and ethical and legal issues, and visualize machine learning models into user interfaces.
After reading this book, you will have a deep understanding of the core concepts of XAI and master multiple XAI tools and methods.
In this book, you'll learn to use Python-related tools and techniques to visualize, interpret, and integrate trustworthy AI results that deliver business value while avoiding common problems with AI bias and ethics and law.
This book will take you through a hands-on Python machine learning project using Python and TensorFlow 2.x. You'll learn how to use WIT, SHAP, LIME, CEM, and other crucial explainable AI tools. You'll learn about tools designed by IBM, Google, Microsoft, and other advanced AI research labs.
We'll introduce you to several open source explainable AI tools for Python that can be used throughout the machine learning project lifecycle. You will learn how to explore machine learning model results, view key variables and variable relationships, detect and deal with bias and ethical and legal issues, and use Python and explainable AI tools to visualize and interpret machine learning model results.
We will build XAI solutions using Python, TensorFlow 2.x, Google Cloud's XAI Platform, and Google Colaboratory.
Excerpt from the preface of "Python Explainable AI (XAI) Combat"
