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|a Feature engineering for machine learning :
|b principles and techniques for data scientists /
|c Alice Zheng and Amanda Casari.
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|a First edition.
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|a 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.
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