Hands-On Explainable AI (XAI) with Python
In today’s era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI’s pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate AI.
Offered by
Difficulty Level
Intermediate
Completion Time
15h8m
Language
English
About Book
Who Is This Book For?
This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysis Data analysts and data scientists who want an introduction into explainable AI tools and techniques AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Hands-On Explainable AI (XAI) with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 15h8m total length
Explaining Artificial Intelligence with Python
White Box XAI for AI Bias and Ethics
Explaining Machine Learning with Facets
Microsoft Azure Machine Learning Model Interpretability with SHAP
Building an Explainable AI Solution from Scratch
AI Fairness with Google's What-If Tool (WIT)
A Python Client for Explainable AI Chatbots
Local Interpretable Model-Agnostic Explanations (LIME)
The Counterfactual Explanations Method
Contrastive XAI
Anchors XAI
Cognitive XAI
Related Resources
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