Hands-On Entity Resolution

Regular price €68.99
Quantity:
Delivery/Collection within 10-20 working days
Shipping & Delivery
A01=Michael Shearer
Age Group_Uncategorized
Age Group_Uncategorized
Author_Michael Shearer
automatic-update
Category1=Non-Fiction
Category=UYQM
COP=United States
Delivery_Delivery within 10-20 working days
Entity Resolution Data Matching Master Data Management Machine Learning Python Risk Management Financial Crime
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Language_English
PA=Available
Price_€50 to €100
PS=Active
softlaunch

Product details

  • ISBN 9781098148485
  • Dimensions: 178 x 233mm
  • Publication Date: 13 Feb 2024
  • Publisher: O'Reilly Media
  • Publication City/Country: US
  • Product Form: Paperback
  • Language: English
Secure checkout Fast Shipping Easy returns

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.

More from this author