[ CourseBoat ] Interpretable Machine Learning with Python - Learn to build interpretable high-performance models

[ CourseBoat.com ] Interpretable Machine Learning with Python: Learn to build interpretable high-performance models

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English | ASIN : B08PDFXXRL | 338 pages | 2021 | True ( PDF , EPUB, MOBI ) | 72.10 MB
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable models

Key Features
Learn how to extract easy-to-understand insights from any machine learning model
Become well-versed with interpretability techniques to build fairer, safer, and more reliable models
Mitigate risks in AI systems before they have broader implications by learning how to debug black-box models
Book Description
Do you want to understand your models and mitigate the risks associated with poor predictions using practical machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you overcome these challenges, using interpretation methods to build fairer and safer ML models.

The first section of the book is a beginner's guide to interpretability and starts by recognizing its relevance in business and exploring its key aspects and challenges. You’ll focus on how white-box models work, compare them to black-box and glass-box models, and examine the trade-offs. The second section will get you up to speed with interpretation methods and how to apply them to different use cases. In addition to the step-by-step code, there's a strong focus on interpreting model outcomes in the context of each chapter's example. In the third section, you’ll focus on tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining.

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