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Item #:
45674217
Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
80% of respondents would recommend this to a friend
Item #:
45674217
MAD 849
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What Stands Out
Comprehensive Learning
Offers in-depth tutorials that cover the interpretation, visualization, and explanation of AI models, ensuring users grasp essential concepts and techniques for applying XAI in real-world scenarios.
Practical Application
Focuses on hands-on projects using Python, empowering learners to develop AI applications that are fair, secure, and trustworthy, making complex topics accessible through engaging, practical examples.
AI Integration
Teaches integration of explainable AI solutions into existing frameworks, helping developers enhance their AI applications' reliability and transparency, which is crucial for gaining user trust and implementing ethical AI practices.
Product Details
| Publisher | Packt Publishing |
| Publication date | July 31, 2020 |
| Language | English |
| Print length | 454 pages |
| ISBN-10 | 1800208138 |
| ISBN-13 | 978-1800208131 |
| Item Weight | 1.71 pounds (780 grams) |
| Dimensions | 7.5 x 1.03 x 9.25 inches (19.1 x 2.6 x 23.5 cm) |
Who Should Buy?
-
Data Scientists
This product provides necessary tools for data scientists to enhance AI interpretability and model trustworthiness in applications.
-
AI Researchers
Ideal for researchers exploring explainability in AI, enabling better insights and communication of complex algorithms.
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Developers
Developers building AI applications will benefit from integrating XAI principles to ensure fair and reliable models.
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Beginners
Users with no prior programming or machine learning experience may struggle with the book's concepts and implementations.
Product Description
Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
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Intelligence & Semantics Editorial Review
Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps is an essential resource in the rapidly advancing field of machine learning and artificial intelligence. This book addresses the critical need for explainable AI, providing insights on techniques for effectively handling data, training models, and making predictions. Readers appreciate the current content covering tools like SHAP and LIME, along with a variety of datasets that offer fresh examples unlike those repeated in other texts. This is particularly useful for practitioners aiming to understand AI biases and ethical considerations crucial in today's technology landscape.
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Pros
- Comprehensive guide to AI explainability techniques.
- Includes practical examples with various datasets.
- Focus on ethical considerations in AI development.
- Covers open-source tools for Python practitioners.
- Accessible language makes complex concepts easier to grasp.
Cons
- Primarily focuses on Python programming only.
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23 October 2026 · via Trustpilot
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