Enrich your knowledge on Explainable AI from my book Applied Machine Learning Explainability Techniques

Learn how to bridge the gap between AI and non-technical consumers of AI by building Explainable AI systems from this book

On July 29th, 2022, my book on Applied Machine Learning Explainability Techniques was launched world wide from Packt publishing on the technical topic of Explainable AI (XAI). XAI is an emerging field for bringing Artificial Intelligence (AI) closer to non-technical end-users. XAI promises to make Machine Learning (ML) models transparent, and trustworthy and promote AI adoption for industrial and research use-cases.

If you are a ML practitioner and you want to make ML models explainable and trustworthy for practical applications, then this book is for you. This book is designed with a unique blend of industrial and academic research perspectives for gaining practical skills in XAI. ML/AI experts working with Data Science, ML, Deep Learning, and AI will be able to put their knowledge to work with this practical guide of XAI for bridging the gap between AI and end-user.

The book provides a hands-on approach for implementation and associated methodologies of XAI that will have them up-and-running, and productive in no time. All the code tutorial providing you hands-on practical exposure to XAI is freely available on Github: https://github.com/PacktPublishing/Applied-Machine-Learning-Explainability-Techniques.

Initially, the readers will get a conceptual understanding of XAI and its necessity. Then, the readers will get the necessary practical exposure to utilize XAI in the AI/ML problem-solving process by making use of state-of-the-art methods and frameworks. Finally, they will get the necessary guidelines to take XAI to the next step and bridge the existing gaps between AI and end-users. By the end of this book, the readers will be able to implement XAI methods and approaches using Python for solving industrial problems, addressing the key pain points encountered, and the best practices in the AI/ML life cycle.

Key features of this book:

• Explore various explainability methods for designing robust and scalable explainable ML systems
• Use XAI frameworks such as LIME and SHAP to make ML models explainable to solve practical problems
• Design user-centric explainable ML systems using guidelines provided for industrial applications

What you will learn

• Explore various explanation methods and their evaluation criteria
• Learn model explanation methods for structured and unstructured data
• Apply data-centric XAI for practical problem-solving
• Hands-on exposure to LIME, SHAP, TCAV, DALEX, ALIBI, DiCE, and others
• Discover industrial best practices for explainable ML systems
• Use user-centric XAI to bring AI closer to non-technical end users
• Address open challenges in XAI using the recommended guidelines

Target Audience

This book is designed for scientists, researchers, engineers, architects, and managers who are actively engaged in the field of Machine Learning and related areas. In general, anyone who is interested in problem-solving using AI would be benefited from this book. The readers are recommended to have a foundational knowledge of Python, Machine Learning, Deep Learning, and Data Science. This book is ideal for readers who are working in the following roles:

• Data and AI Scientists
• AI/ML Engineers
• AI/ML Product Managers
• AI Product Owners
• AI/ML Researchers
• User experience and HCI Researchers

Table of Contents

1. Foundational Concepts of Explainability Techniques
2. Model Explainability Methods
3. Data-Centric Approaches
4. LIME for Model Interpretability
5. Practical Exposure to Using LIME in ML
6. Model Interpretability Using SHAP
7. Practical Exposure to Using SHAP in ML
8. Human-Friendly Explanations with TCAV
9. Other Popular XAI Frameworks
10. XAI Industry Best Practices
11. End User-Centered Artificial Intelligence

Editorial Reviews

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Essential Links

1. Amazon.com – https://amzn.to/3gKUm5M
2. Amazon.in – https://amzn.to/3W4YopP
3. Amazon.co.uk – https://amzn.to/3U1kSXd
4. Packt Publishing – https://www.packtpub.com/product/applied-machine-learning-explainability-techniques/9781803246154
5. GitHub Code Tutorial – https://github.com/PacktPublishing/Applied-Machine-Learning-Explainability-Techniques

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