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Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples
88% of respondents would recommend this to a friend
USD 76
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Learn how to extract easy-to-understand insights from any machine learning model and leverage interpretability techniques to build fairer, safer, and more reliable models
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What Stands Out
Detalles de producto
- Comprehensive hands-on guide to machine learning interpretability with real-world examples
- Covers fundamentals and challenges in interpretation for designing systems with fairness, accountability, and transparency
- Addresses topics like White Box and Glass Box Models, Feature Importance, Bias Mitigation Methods, and more
- Goes beyond transparency to cover fairness and accountability, often ignored by other books in the field
- Authored by an experienced developer with 15 years of development experience and insight into the importance of model trust and reliability
- Aims to help readers understand that interpretability is essential for predictive and prescriptive analytics beyond just optimizing predictive performance
| Publisher | Packt Publishing |
| Publication date | March 26, 2021 |
| Language | English |
| Print length | 736 pages |
| ISBN-10 | 180020390X |
| ISBN-13 | 978-1800203907 |
| Item Weight | 2.74 pounds (1.24 kg) |
| Dimensions | 7.5 x 1.66 x 9.25 inches (19.1 x 4.2 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to enhance model interpretability while maintaining high performance in their analyses.
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Machine Learning Practitioners
Perfect for ML practitioners looking to implement interpretable models in real-world scenarios with practical examples.
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Academic Researchers
Beneficial for researchers in academia focused on making machine learning outputs more interpretable for scholarly work.
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Beginners in Programming
Not suitable for individuals without a programming background, as prior knowledge in Python is necessary for effective learning.
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Expert Systems Editorial Review
"Interpretable Machine Learning with Python" is a highly recommended resource for anyone looking to understand and build interpretable machine learning models. The book covers a thorough introduction to ML evaluation metrics and bias in ML. It provides examples and python code on how to build and interpret metrics. The book is technical but also goes through the ideas behind the examples and covers post-hoc methods to explain machine learning techniques. The book is lengthy but is well explained for beginners while also providing enough gold nuggets of information spread throughout the book for more intermediates and advanced learners. The book provides abundant examples and a comprehensive reference for tackling interpretable machine learning. Recommended to get the ebook.
Customer Reviews & Ratings
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5 estrella
73%
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4 estrella
12%
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3 estrella
5%
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2 estrella
5%
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1 estrella
5%
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ventajas
- Thorough introduction to ML evaluation metrics
- Covers the elements to interpret metrics
- Examples and python code provided
- Covers post-hoc methods to explain machine learning techniques
- Comprehensive reference for tackling interpretable machine learning
- Abundant examples
Contras
- Proofreading should be improved to correct typos
Product Price History
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características y beneficios
- Understand ML interpretability and its relevance in business
- Learn model-agnostic interpretability methods and visualizations
- Discover how to mitigate bias and enhance model reliability
- Range of techniques from feature selection to adversarial training
- Ideal for data scientists and ML developers with Python skills
- Applied guidance for building fair, safer and reliable models
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