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Feature Engineering for Machine Learning de Alice Zheng

Feature Engineering for Machine Learning

Autor Alice Zheng
en Limba Engleză Paperback – 9 apr 2018
"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.
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Specificații

ISBN-13: 9781491953242
ISBN-10: 1491953241
Pagini: 630
Dimensiuni: 180 x 238 x 14 mm
Greutate: 0.4 kg
Editura: O'Reilly

Descriere

Feature engineering is essential to applied machine learning, but using domain knowledge to strengthen your predictive models can be difficult and expensive. To help fill the information gap on feature engineering, this complete hands-on guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic.Author Alice Zheng explains common practices and mathematical principles to help engineer features for new data and tasks. If you understand basic machine learning concepts like supervised and unsupervised learning, you re ready to get started.

Not only will you learn how to implement feature engineering in a systematic and principled way, you ll also learn how to practice better data science. * Learn exactly what feature engineering is, why it s important, and how to do it well* Use common methods for different data types, including images, text, and logs* Understand how different techniques such as feature scaling and principal component analysis work* Understand how unsupervised feature learning works in the case of deep learning for images"

Notă biografică

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.