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Applied Machine Learning Explainability Techniques
Applied Machine Learning Explainability Techniques

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

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Profile Icon Aditya Bhattacharya
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$48.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (27 Ratings)
Paperback Jul 2022 306 pages 1st Edition
eBook
$9.99 $39.99
Paperback
$48.99
Arrow left icon
Profile Icon Aditya Bhattacharya
Arrow right icon
$48.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (27 Ratings)
Paperback Jul 2022 306 pages 1st Edition
eBook
$9.99 $39.99
Paperback
$48.99
eBook
$9.99 $39.99
Paperback
$48.99

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Applied Machine Learning Explainability Techniques

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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
Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free $6.95

Premium delivery 6 - 9 business days

$21.95
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Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jul 29, 2022
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246154
Category :

What do you get with Print?

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Product feature icon AI Assistant (beta) to help accelerate your learning
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Estimated delivery fee Deliver to United States

Economy delivery 10 - 13 business days

Free $6.95

Premium delivery 6 - 9 business days

$21.95
(Includes tracking information)

Product Details

Publication date : Jul 29, 2022
Length: 306 pages
Edition : 1st
Language : English
ISBN-13 : 9781803246154
Category :

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Table of Contents

15 Chapters
Section 1 – Conceptual Exposure Chevron down icon Chevron up icon
Chapter 1: Foundational Concepts of Explainability Techniques Chevron down icon Chevron up icon
Chapter 2: Model Explainability Methods Chevron down icon Chevron up icon
Chapter 3: Data-Centric Approaches Chevron down icon Chevron up icon
Section 2 – Practical Problem Solving Chevron down icon Chevron up icon
Chapter 4: LIME for Model Interpretability Chevron down icon Chevron up icon
Chapter 5: Practical Exposure to Using LIME in ML Chevron down icon Chevron up icon
Chapter 6: Model Interpretability Using SHAP Chevron down icon Chevron up icon
Chapter 7: Practical Exposure to Using SHAP in ML Chevron down icon Chevron up icon
Chapter 8: Human-Friendly Explanations with TCAV Chevron down icon Chevron up icon
Chapter 9: Other Popular XAI Frameworks Chevron down icon Chevron up icon
Section 3 –Taking XAI to the Next Level Chevron down icon Chevron up icon
Chapter 10: XAI Industry Best Practices Chevron down icon Chevron up icon
Chapter 11: End User-Centered Artificial Intelligence Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(27 Ratings)
5 star 88.9%
4 star 7.4%
3 star 3.7%
2 star 0%
1 star 0%
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Adi Jul 29, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Amazon
Ayan sarkar Jul 31, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
It's a amazing book and it is highly informative. The book is very much detailed on every topic and it is highly recommended.
Amazon Verified review Amazon
Anuradha Bhattacharya Jul 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Arish Roy Jul 30, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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 Verified review Amazon
Amazon Customer Sep 20, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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