The Art of Feature Engineering: Essentials for Machine Learning

Duboue, Pablo Pablo Duboue,

ISBN 10: 1108709389 / ISBN 13: 9781108709385

Published by Cambridge University Press, 2020

About this Item

The Art of Feature Engineering: Essentials for Machine Learning This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. .

Seller Inventory # 7719-9781108709385

Bibliographic Details

• Title: The Art of Feature Engineering: Essentials for Machine Learning
• Publisher: Cambridge University Press
• Publication Date: 2020
• Language: English
• ISBN 10: 1108709389
• ISBN 13: 9781108709385
• Binding: paperback
• Condition: Very Good

About this title

Synopsis

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

About the Author

Pablo Duboue is Director of Textualization Software Ltd. and is passionate about improving society through technology. He has a Ph.D. in Computer Science from Columbia University and was part of the IBM Watson team that beat the Jeopardy! Champions in 2011. He splits his time between teaching machine learning, doing open research, contributing to free software projects, and consulting for start-ups. He has taught in three different countries and done joint research with more than fifty co-authors. Recent career highlights include a best paper award in the Canadian AI conference industrial track and consulting for a start-up acquired by Intel Corp.