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- Author: Alice Zheng, Amanda Casari
- Publisher: O'Reilly UK Ltd.
- Year: 2018
- Pages: 218
- ISBN-10: 1491953241
- ISBN-13: 9781491953242
- Format: 17.8 x 23.8 x 1.8 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Feature Engineering for Machine Learning (e-book) (used book) | bookbook.eu
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Description
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.
Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.
EXTRA 10 % discount with code: EXTRA
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- Author: Alice Zheng, Amanda Casari
- Publisher: O'Reilly UK Ltd.
- Year: 2018
- Pages: 218
- ISBN-10: 1491953241
- ISBN-13: 9781491953242
- Format: 17.8 x 23.8 x 1.8 cm, softcover
- Language: English English
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering.
Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples.
Reviews