Binary Classification for Entity Resolution

In today's data-driven world, identifying duplicate records and matching entities across disparate databases is a critical challenge. This text-based course guides you through the foundational concepts of entity resolution using binary classification, showing you how to determine whether two records represent the same real-world object. You will learn to navigate the unique challenges of this domain, including extreme class imbalance where non-matches vastly outnumber matches. By the end of this course, you will understand how to design, evaluate, and deploy robust entity resolution workflows using both deterministic rules and modern learning-based models. What you'll learn: Understand core entity resolution terminology and data matching pipelines; Implement rule-based heuristics and string similarity metrics for baseline matching; Apply binary classification algorithms to learn matching patterns from training data; Manage severe class imbalance using sampling, thresholding, and cost-sensitive learning; Evaluate model performance accurately using precision, recall, F1-score, and confusion matrices; Explore modern practices including blocking techniques and scalable data preparation. The course begins with essential definitions and data-matching concepts before progressing to practical classification strategies and evaluation metrics. Designed for data analysts, software developers, and aspiring data scientists, this course requires only basic programming familiarity and no prior machine learning experience. Start mastering the art of clean data today.