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ic 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