Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists

"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 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."--Page 4 of cover.

About the author

Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.

Summary

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.

Product details

• Authors: Amanda Casari, Alice Zheng, Zheng Alice
• Publisher: O'Reilly
• Languages: English
• Product format: Paperback / Softback
• Released: 30.04.2018
• EAN: 9781491953242
• ISBN: 978-1-4919-5324-2
• Dimensions: 180 mm x 232 mm x 12 mm
• Weight: 400 g
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; COMPUTERS / Computer Science, computer science, Databases, COMPUTERS / Database Administration & Management, COMPUTERS / Data Science / Data Warehousing