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Feature Engineering For Machine Learning : Principles And Techniques For Data Scientists (2018)
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Call Number 006.31/ZHENG,A


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LocationCall NumberItem Status
Adult Nonfiction006.31/ZHENG,ADue 09-07-18
Published: Beijing: O'Reilly, 2018
Edition:  First edition
Description:  xiii, 200 pages : illustrations ; 24 cm
ISBN/ISSN: 9781491953242, 1491953241, 9781491953242,
Language:  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, 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



The machine learning pipeline -- Fancy tricks with simple numbers -- Text data : flattening, filtering, and chunking -- The effects of feature scaling : from bag-of-words to Tf-Idf -- Categorical variables : counting eggs in the age of robotic chickens -- Dimensionality reduction : squashing the data pancake with PCA -- Nonlinear featurization via K-means model stacking -- Automating the featurizer : image feature extraction and deep learning -- Back to the feature : building an academic paper recommender -- Linear modeling and linear algebra basics

Related Searches:
Machine learning
Data mining
Added--201806 anf

Additional Credits:
Casari, Amanda, author

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