Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning is a practical resource for data scientists who want to understand how meaningful features support predictive models. Alice Zheng and Amanda Casari explain core principles and techniques for preparing and transforming data, making this book a useful guide to an essential part of machine learning.
About This Book
Feature Engineering for Machine Learning presents principles and techniques for preparing data for machine learning.
Written for data scientists, the book focuses on transforming information into useful features for predictive modeling.
It offers a practical foundation for understanding the role of feature engineering in machine learning workflows.
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