Deep Matching for Entity Resolution with BERT

Data is often messy, inconsistent, and spread across multiple databases with different formats. Entity resolution—the process of identifying when different records refer to the same real-world entity—is critical for clean data, but traditional rule-based methods struggle with semantic variation. This text-based course guides you through modern deep matching techniques to solve complex entity resolution challenges. You will learn how to leverage deep learning architectures and transformer models to match records with high precision and recall, moving beyond simple string comparison to semantic understanding. What you'll learn: Understand the fundamental concepts of entity resolution, blocking, and matching workflows; Apply deep learning architectures to frame entity resolution as a binary classification problem; Configure and fine-tune BERT models for semantic record matching; Implement modern vector database techniques for efficient candidate generation and blocking; Evaluate matching performance using precision, recall, and F1-score metrics; Optimize inference pipelines to process large-scale datasets efficiently. You will start with essential data matching definitions and traditional blocking methods before transitioning to deep learning solutions. Through clear explanations and written code walkthroughs, you will progress to building and evaluating transformer-based matching pipelines. This course is designed for beginner data scientists, software engineers, and data analysts who want to learn modern record-linking techniques. Basic familiarity with Python is helpful, but no prior background in deep learning is required. Start reading today to master the art of semantic data matching.