Feature Engineering for Machine Learning and Data Analytics
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Feature Engineering for Machine Learning and Data Analytics
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Feature Engineering for Machine Learning and Data Analytics
Description
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.
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How accurate is availability?
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Delivery
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- Separate orders can’t be combined once they’ve been placed, and adding or swapping items can change the delivery estimate.
- Supplier delays: Occasionally a supplier is delayed, or their stock information turns out to be inaccurate. We’ll keep your order open for up to 60 days. If an item hasn’t shipped or isn’t on its way to us by then, we’ll cancel that item and refund it.
Faulty or damaged items are always covered. See Returns.
Returns
Damaged or incorrect items (within 30 days)
If an item arrives damaged or isn’t what you ordered, contact us within 30 days of delivery. We may ask for a photo, and if we need the item back we’ll send a prepaid return label, so it costs you nothing. You’ll receive a replacement or refund, as guaranteed under the Australian Consumer Law.
Change of mind (within 30 days)
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- you have your proof of purchase;
- the item is unused and in its original condition and packaging;
- it isn’t a sale item, special order, custom item or gift voucher.
You pay the postage to return change-of-mind items. Change-of-mind returns aren’t available for print-on-demand titles or bulk orders with special pricing.
Returned to sender
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