Feature Engineering for Machine Learning (O'Reilly)
Friday, 28 September 2018

This practical book demonstrates 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.

<ASIN:1491953241>

Author: Alice Zheng and Amanda Casari
Publisher: O'Reilly
Date: April 2018
Pages: 218
ISBN: 978-1491953242
Print: 1491953241
Kindle: B07BNX4MWC
Audience: Developers working in machine learning
Level: Intermediate
Category: Artificial Intelligence

 

  • 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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