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

Amazon.com Price:  $36.92 (as of 05/05/2019 14:14 PST- Details)

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Description

Feature engineering is a a very powerful step in the machine-learning pipeline, yet this topic is rarely examined by itself. 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. Every chapter guides you through a single data problem, such as methods to represent text or image data. Together, these examples illustrate the primary principles of feature engineering.

Rather than simply teach these principles, authors Alice Zheng and Amanda Casari center of attention on practical application with exercises during the book. The closing chapter brings the whole lot 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 getting rid of 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, the usage of k-means as a featurization technique
  • Image feature extraction with manual and deep-learning techniques

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