Feature engineering for machine learning : Principles and techniques for data scientists
By: Zheng, Alice
.
Contributor(s): Casari, Amanda.
Publisher: Navi Mumbai 2018Edition: 1st.Description: xii, 200p. | Binding - Paperback | 23*17.9 cm.ISBN: 9789352137114.Subject(s): EXTC EngineeringDDC classification: 006.31 Summary: 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 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. Youll 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
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Text Books
|
School of Engineering & Technology Reference Section | Reference | 006.31 ZHE/CAS (Browse shelf) | Not For Loan | E14534 |
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 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. Youll 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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