Interpretability and Explainability in AI Using Python : Decrypt AI Decision-Making Using Interpretability and Explainability with Python to Build Reliable Machine Learning Systems (English Edition).

Format:

Book

Author/Creator:

Chakkirala, Aruna.

Language:

English

Subjects (All):

Machine learning.

Artificial intelligence.

Physical Description:

1 online resource (187 pages)

Edition:

1st ed.

Place of Publication:

Delhi : Orange Education PVT Ltd, 2025.

Summary:

Interpretability in AI/ML refers to the ability to understand and explain how a model arrives at its predictions. It ensures that humans can follow the model's reasoning, making it easier to debug, validate, and trust. Interpretability and Explainability in AI Using Python takes you on a structured journey through interpretability and explainability techniques for both white-box and black-box models. You'll start with foundational concepts in interpretable machine learning, exploring different model types and their transparency levels. As you progress, you'll dive into post-hoc methods, feature effect analysis, anchors, and counterfactuals--powerful tools to decode complex models. The book also covers explainability in deep learning, including Neural Networks, Transformers, and Large Language Models (LLMs), equipping you with strategies to uncover decision-making patterns in AI systems. Through hands-on Python examples, you'll learn how to apply these techniques in real-world scenarios. By the end, you'll be well-versed in choosing the right interpretability methods, implementing them efficiently, and ensuring AI models align with ethical and regulatory standards--giving you a competitive edge in the evolving AI landscape.

Contents:

Cover Page

Title Page

Copyright Page

Dedication Page

About the Author

About the Technical Reviewer

Acknowledgements

Foreword

Preface

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Errata

Table of Contents

1. Interpreting Interpretable Machine Learning

Introduction

Structure

All Pervasive Machine Learning

Machine Learning Methods

Supervised Machine Learning

Classification

Regression

Unsupervised Learning

Clustering

Dimensionality Reduction and Feature Learning

Association Rule Learning

Anomaly Detection

Semi-Supervised Learning

Reinforcement Learning

Deep Learning

The Age of the Transformer

Generative AI Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

9789348107749

9348107747

OCLC:

1515461637