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Key benefits

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

Description

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases. Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users. By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

Who is this book for?

This book is for scientists, researchers, engineers, architects, and managers who are actively engaged in machine learning and related fields. Anyone who is interested in problem-solving using AI will benefit from this book. Foundational knowledge of Python, ML, DL, and data science is recommended. AI/ML experts working with data science, ML, DL, and AI will be able to put their knowledge to work with this practical guide. This book is ideal for you if you're a data and AI scientist, AI/ML engineer, AI/ML product manager, AI product owner, AI/ML researcher, and UX and HCI researcher.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Applied Machine Learning Explainability Techniques

€36.99

Deep Learning and XAI Techniques for Anomaly Detection

€36.99

Interpretable Machine Learning with Python

€38.99

Total € 112.97

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Amazon verified reviews

Adi Jul 29, 2023

Highly recommended for AI developers, researchers or product managers. This excellent book on Explainable AI is for you! The author of this book has carefully articulated the best practices to apply XAI to build scalable and trustworthy AI applications. Explainable AI is the future of AI and this book is the blue print towards Explainable Machine Learning systems. The code resources available on GitHub is a great plus for this book.

Amazon Verified review

Ayan sarkar Jul 31, 2022

It's a amazing book and it is highly informative. The book is very much detailed on every topic and it is highly recommended.

Anuradha Bhattacharya Jul 30, 2022

This is one of the best books, in my opinion, for both experts and novices who have a genuine interest in the subject. The author discusses XAI, SHAP, and other frameworks in great detail. Each chapter offers helpful real-world information, justifications, references, and sample scripts to support the application of XAI concepts. Overall, the book's topics are succinctly described with examples from actual situations and codes.

Arish Roy Jul 30, 2022

This book is one of the most finest book in the field of Machine Learning that lays the foundation and then build upon it. Each chapter will provide you with valuable practical knowledge along with code samples and all contents are explained deeply. Must have book for ML enthusiasts.

Amazon Customer Sep 20, 2022

Modern ML model explanations are a big challenge at current time.This book is about Explainable AI (XAI) field, it make machine learning (ML) modelstransparent, trustworthy and promote AI adoption for industry and researchers. It offers some practical skills in XAI, LIME and SHAP, it is good for both ML engineer and ML scientist.The book introduced some popular frameworks below:1. LIME, one of the most popular XAI frameworks. The book uses two chapters to describe how to use LIME for images, texts along with structured tabular data, to build ML models, such as classification and regression models.2. SHAP included explainers such as TreeExplainer, DeepExplainer, GradientExplainer, KernelExplainer and LinearExplainer. The book introduces lots of plots, such as feature importance bar plots, cohort plots, heatmap plots, summary plots, dependence plots, waterfall plots, force plots, decision plots.3. Other frameworks, like TCAV, DALEX, Explainer Dashboard, InterpretML, ALIBI, DiCE, ELI5 and H2O AutoML explainer.Personally, I suggest people read this book if you are interested in this field.

Applied Machine Learning Explainability Techniques: Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

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