Feature engineering for machine learning : principles and techniques for data scientists / Alice Zheng and Amanda Casari.
Material type:
TextPublication details: Beijing : Boston O'Reilly, 2018Edition: 1st edDescription: xiii, 200 p.: ill. ; 24 cmISBN: 9781491953242 (pbk.); 1491953241 (pbk.)Subject(s): Machine learning | Data miningDDC classification: 006.31 LOC classification: Q325.5 | .Z44 2018Summary: Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto 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.--
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
Staff Circulation
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APU Library Open Shelf | Book | Q325.5 .Z44 2018 c.1 (Browse shelf (Opens below)) | 1 | Available | 00012388 | |
General Circulation
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APU Library Open Shelf | Book | Q325.5 .Z44 2018 c.2 (Browse shelf (Opens below)) | 2 | Available | 00012389 |
Includes bibliographical references and index.
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll learn techniques for extracting and transforming featuresthe numeric representations of raw datainto 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.--
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