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The Art of Feature Engineering

The Art of Feature Engineering
Essentials for Machine Learning

c.$49.99 ( )

  • Publication planned for: July 2020
  • availability: Not yet published - available from July 2020
  • format: Paperback
  • isbn: 9781108709385

c.$ 49.99 ( )

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About the Authors
  • 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.

    • Helps the practitioner obtain better end-to-end performance by not focusing on just tuning model parameters
    • Can be used as a supplementary text for machine learning or data science courses
    • Presents hands-on case studies, with 200 accompanying programs for students and instructors
    • Includes a first-of-its-kind publicly available dataset for teaching feature engineering
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    Product details

    • Publication planned for: July 2020
    • format: Paperback
    • isbn: 9781108709385
    • dimensions: 228 x 152 mm
    • availability: Not yet published - available from July 2020
  • Table of Contents

    Part I. Fundamentals:
    1. Introduction
    2. Features, combined
    3. Features, expanded
    4. Features, reduced
    5. Advanced topics
    Part II. Case Studies:
    6. Graph data
    7. Timestamped data
    8. Textual data
    9. Image data
    10. Other domains.

  • Resources for

    The Art of Feature Engineering

    Pablo Duboue

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  • Author

    Pablo Duboue, Textualization Software Ltd.
    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.

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