Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
Language: English
Published by O'reilly Media Mai 2018, 2018
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Neuware - 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 featuresthe numeric representations of raw datainto 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.…
Seller Inventory # 9781491953242
- Title
- Feature Engineering for Machine Learning : Principles and Techniques for Data Scientists
- Author
- Alice Zheng
- Publisher
- O'reilly Media Mai 2018
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1491953241
- ISBN 13
- 9781491953242
- Item weight
- 399 grams
- Dimensions
- 238x178x18 mm
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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About the Author
"About the title" may belong to another edition of this title.
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