A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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