Items related to Hands-On Entity Resolution: A Practical Guide to Data...
- PublisherO'Reilly Media
- Publication date2024
- ISBN 10 1098148487
- ISBN 13 9781098148485
- BindingPaperback
- LanguageEnglish
- Number of pages196
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Hands-On Entity Resolution: A Practical Guide to Data Matching with Python
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O'Reilly Media (edition 1), 2024
ISBN 10: 1098148487
ISBN 13: 9781098148485
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Hands-On Entity Resolution: A Practical Guide to Data Matching with Python
Published by
O'Reilly Media (edition 1), 2024
ISBN 10: 1098148487
ISBN 13: 9781098148485
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Hands-On Entity Resolution : A Practical Guide to Data Matching With Python
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Hands-On Entity Resolution: A Practical Guide to Data Matching with Python (Paperback or Softback)
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Hands-On Entity Resolution: A Practical Guide to Data Matching With Python
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Hands-On Entity Resolution
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Hands-On Entity Resolution
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Hands-On Entity Resolution (Paperback)
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O'Reilly Media, Sebastopol, 2024
ISBN 10: 1098148487
ISBN 13: 9781098148485
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Paperback. Condition: new. Paperback. Entity resolution is a key analytic technique that enables you to identify multiple data records that refer to the same real-world entity. With this hands-on guide, product managers, data analysts, and data scientists will learn how to add value to data by cleansing, analyzing, and resolving datasets using open source Python libraries and cloud APIs. Author Michael Shearer shows you how to scale up your data matching processes and improve the accuracy of your reconciliations. You'll be able to remove duplicate entries within a single source and join disparate data sources together when common keys aren't available. Using real-world data examples, this book helps you gain practical understanding to accelerate the delivery of real business value. With entity resolution, you'll build rich and comprehensive data assets that reveal relationships for marketing and risk management purposes, key to harnessing the full potential of ML and AI. This book covers: Challenges in deduplicating and joining datasets Extracting, cleansing, and preparing datasets for matching Text matching algorithms to identify equivalent entities Techniques for deduplicating and joining datasets at scale Matching datasets containing persons and organizations Evaluating data matches Optimizing and tuning data matching algorithms Entity resolution using cloud APIs Matching using privacy-enhancing technologies About the Author Michael Shearer is the Group Head of Compliance Product Management for HSBC. Since joining HSBC in 2014 he has led the delivery of financial crime risk capabilities for the bank, including industry-leading artificial intelligence and network analytics platforms. Prior to HSBC Michael spent 20 years in UK government service where he led the delivery of international projects to acquire and process large volumes of highly sensitive data. Michael is a Chartered Engineer. He was educated at Queen's University Belfast where he gained a Master's degree in Electrical and Electronic Engineering with distinction. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781098148485
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