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Viser: Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists

Feature Engineering for Machine Learning, 1. udgave
Søgbar e-bog

Feature Engineering for Machine Learning Vital Source e-bog

Alice Zheng og Amanda Casari
(2018)
O'Reilly Media, Inc
399,00 kr.
Leveres umiddelbart efter køb
Feature Engineering for Machine Learning - Principles and Techniques for Data Scientists, 1. udgave

Feature Engineering for Machine Learning

Principles and Techniques for Data Scientists
Alice Zheng og Amanda Casari
(2018)
Sprog: Engelsk
O'Reilly Media, Incorporated
598,00 kr.
ikke på lager, Bestil nu og få den leveret
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Detaljer om varen

  • 1. Udgave
  • Vital Source searchable e-book (Reflowable pages)
  • Udgiver: O'Reilly Media, Inc (Marts 2018)
  • Forfattere: Alice Zheng og Amanda Casari
  • ISBN: 9781491953198
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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Detaljer om varen

  • 1. Udgave
  • Paperback: 630 sider
  • Udgiver: O'Reilly Media, Incorporated (April 2018)
  • Forfattere: Alice Zheng og Amanda Casari
  • ISBN: 9781491953242

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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