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Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples
88% of respondents would recommend this to a friend
€ 34
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A deep dive into the key aspects and challenges of machine learning interpretability using a comprehensive toolkit to build fairer, safer, and more reliable models.
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What Stands Out
Product Details
| Publisher | Packt Publishing |
| Publication date | October 31, 2023 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 606 pages |
| ISBN-10 | 180323542X |
| ISBN-13 | 978-1803235424 |
| Item Weight | 2.26 pounds (1.03 kg) |
| Dimensions | 7.5 x 1.37 x 9.25 inches (19.1 x 3.5 x 23.5 cm) |
Who Should Buy?
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Data Scientists
Ideal for data scientists seeking to enhance the interpretability and fairness of their machine learning models.
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AI Researchers
Perfect resource for AI researchers focused on developing explainable algorithms and understanding model behavior in real-world applications.
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Educators
Suitable for educators teaching machine learning concepts, emphasizing practical, hands-on learning with interpretable models.
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Beginners
Not suitable for absolute beginners without prior knowledge of machine learning concepts and Python programming.
Product Description
Interpretable Machine Learning with Python: Build explainable, fair, and robust high-performance models with hands-on, real-world examples
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Expert Systems Editorial Review
**** "Interpretable Machine Learning with Python - Second Edition," authored by Serg Masís and published by Packt, has garnered widespread acclaim for its comprehensive and practical approach to demystifying the complexities of machine learning model interpretation. The structure of the book, built around distinct "missions," enhances the reader's engagement by combining theoretical concepts with hands-on Python code and illustrative plots. Such an approach is particularly beneficial for both beginners and advanced practitioners, making the content accessible while also offering depth. One of the book’s key strengths lies in its emphasis on explainability, interpretability, and their implications for fairness and reliability in machine learning. Readers are introduced to essential terminologies and different model types, including black-box, white-box, and glass-box models. Practical applications, such as the exploration of bias in datasets, further emphasize the importance of understanding machine learning outcomes in real-world scenarios. The author masterfully bridges theory with application, illustrating how shortcomings in model interpretability can manifest in critical real-life decision-making processes. The breadth of topics covered, ranging from traditional methods of interpretation to complex architectures like convolutional neural networks and natural language processing, ensures that the book remains relevant in the fast-evolving landscape of machine learning. The included case studies and additional resources, such as the active Discord community, enhance learning by fostering collaboration and discussion among readers. This book not only serves as a foundational resource but also challenges practitioners to actively engage with interpretable machine learning tools, reinforcing the notion that understanding the "why" behind model decisions is paramount. With its combination of detailed explanations, practical examples, and strategic guidance on avoiding pitfalls, "Interpretable Machine Learning with Python" stands out as an essential read for anyone looking to grasp the intricacies of machine learning interpretation. **
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Pros
- Structured approach with clear "missions" enhancing engagement.
- Comprehensive coverage of key interpretability concepts and tools.
- Practical applications with real-world case studies.
- Suitable for beginners and advanced readers alike.
- Active support community via Discord.
- Offers valuable insights into advanced topics like CNNs and NLP.
Cons
- Lengthy due to its ambitious scope, which may overwhelm some readers.
Product Price History
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€ 34
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Features & Benefits
- Helps make machine learning models more robust, transparent, and fair
- Covers interpretability methods for white-box and black-box models
- Provides specific methods for deep learning models in vision, text, and time series domains
- Offers advice for companies using black-box models to prioritize transparency
- Focuses on explaining model decisions to uncover and mitigate biases
- Includes key features like SHAP, feature importance, and causal inference
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