Bhattacharya, Aditya
Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more
- ISBN 13:
- 9781803246154
- author:
- Bhattacharya, Aditya
- format:
- Paperback
- publisher:
- Packt Publishing Limited
- language:
- Spanish
- Publication Year:
- 2022
- Pages:
- 304
- Dimensions:
- 1.7 x 19.1 x 19.1 centimetres (0
- Genre:
- Computers, Artificial Intelligence, General (US: Trade),
- Condition:
- New
- Availability:
- Item usually sent within 7 working days
Description
Applied Machine Learning Explainability Techniques is a practical guide to making machine learning models transparent and trustworthy. By leveraging top XAI frameworks such as LIME and SHAP, you'll gain the skills needed to design robust and scalable explainable ML systems.
This book combines industrial and academic research perspectives to help you acquire practical XAI skills. You'll learn how to apply state-of-the-art methods and frameworks using Python to solve industrial problems and address key pain points encountered in AI/ML problem-solving processes.
By the end of this book, you'll be equipped with best practices in the AI/ML life cycle and will have the essential guidelines needed to take your XAI journey to the next level. With this knowledge, you'll be able to implement XAI methods and approaches effectively, making machine learning models explainable and trustworthy for practical applications.
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