2018. Paperback. 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 this complete guide teaches beginning-to-intermediate data scientists how to work with this widely practiced but little discussed topic. Num Pages: 200 pages. BIC Classification: UY. Category: (P) Professional & Vocational. Dimension: 233 x 178 x 15. Weight in Grams: 666. . . . . . Books ship from the US and Ireland.

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Detalles bibliogr�ficos

T�tulo: Feature Engineering for Machine Learning
Editor: O Reilly Media, Inc, USA
Fecha de publicaci�n: 2018
Idioma: Ingl�s
ISBN 10: 1491953241
ISBN 13: 9781491953242
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

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.

You'll examine:

Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms
Natural text techniques: bag-of-words, n-grams, and phrase detection
Frequency-based filtering and feature scaling for eliminating uninformative features
Encoding techniques of categorical variables, including feature hashing and bin-counting
Model-based feature engineering with principal component analysis
The concept of model stacking, using k-means as a featurization technique
Image feature extraction with manual and deep-learning techniques

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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.

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