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Feature Engineering for Machine Learning - by Alice Zheng & Amanda Casari (Paperback)

Feature Engineering for Machine Learning - by  Alice Zheng & Amanda Casari (Paperback) - 1 of 1
$56.99 sale price when purchased online
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About this item

Highlights

  • Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own.
  • About the Author: Alice is a technical leader in the field of Machine Learning.
  • 215 Pages
  • Computers + Internet, Databases

Description



About the Book



"Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, youll 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."--Page 4 of cover.



Book Synopsis



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



About the Author



Alice is a technical leader in the field of Machine Learning. Her experience spans algorithm and platform development and applications. Currently, she is a Senior Manager in Amazon's Ad Platform. Previous roles include Director of Data Science at GraphLab/Dato/Turi, machine learning researcher at Microsoft Research, Redmond, and postdoctoral fellow at Carnegie Mellon University. She received a Ph.D. in Electrical Engineering and Computer science, and B.A. degrees in Computer Science in Mathematics, all from U.C. Berkeley.

Dimensions (Overall): 9.1 Inches (H) x 7.0 Inches (W) x .4 Inches (D)
Weight: .7 Pounds
Suggested Age: 22 Years and Up
Sub-Genre: Databases
Genre: Computers + Internet
Number of Pages: 215
Publisher: O'Reilly Media
Theme: Data Mining
Format: Paperback
Author: Alice Zheng & Amanda Casari
Language: English
Street Date: May 8, 2018
TCIN: 83304119
UPC: 9781491953242
Item Number (DPCI): 247-57-1491
Origin: Made in the USA or Imported
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Shipping details

Estimated ship dimensions: 0.4 inches length x 7 inches width x 9.1 inches height
Estimated ship weight: 0.7 pounds
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