Feature Engineering for Machine Learning and Data Analytics

Business & Finance, Economics, Statistics, Nonfiction, Computers, Entertainment & Games, Game Programming - Graphics, Database Management
Cover of the book Feature Engineering for Machine Learning and Data Analytics by , CRC Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781351721264
Publisher: CRC Press Publication: March 14, 2018
Imprint: CRC Press Language: English
Author:
ISBN: 9781351721264
Publisher: CRC Press
Publication: March 14, 2018
Imprint: CRC Press
Language: English

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.

The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.

The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.

This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation.

The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.

The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.

This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.

More books from CRC Press

Cover of the book Interactive Design for New Media and the Web by
Cover of the book Statutory Valuations by
Cover of the book Law for the Construction Industry by
Cover of the book Plant Viruses by
Cover of the book Soil Degradation and Restoration in Africa by
Cover of the book Designing with Solar Power by
Cover of the book Interaction of Railway Vehicles with the Track and Its Substructure by
Cover of the book Silicon Earth by
Cover of the book Air Rage by
Cover of the book Total Quality Management for Project Management by
Cover of the book Extending Moore's Law through Advanced Semiconductor Design and Processing Techniques by
Cover of the book Elementary Structural Analysis and Design of Buildings by
Cover of the book Super-Resolution Imaging by
Cover of the book Geotechnical Engineering for Transportation Infrastructure by
Cover of the book LiDAR Remote Sensing and Applications by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy