Feature engineering for machine learning : principles and techniques for data scientists

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, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chap...

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Detalles Bibliográficos
Otros Autores: Zheng, Alice, author (author), Casari, Amanda, author
Formato: Libro electrónico
Idioma:Inglés
Publicado: Beijing : O'Reilly [2018]
Edición:First edition
Materias:
Machine learning.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631108606719
Tabla de Contenidos:
  • 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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