Entity Resolution with Markov Clustering in Python

In a world of messy, disconnected data, identifying duplicate records and linking identities across systems is a critical challenge. Traditional record-matching methods often struggle with complex, overlapping relationships, which is where graph-based clustering shines. This text-based course guides you through using Markov Clustering (MCL) to dramatically improve the accuracy of your entity resolution workflows. Through clear, step-by-step written explanations and clean code examples, you will transform how you handle duplicate data. You will start with the foundational concepts of graph theory and entity matching, then progress to implementing and fine-tuning clustering algorithms using modern Python development practices. What you will learn: - Understand the core terminology of entity resolution, graph theory, and Markov chains - Build a robust entity resolution pipeline using clean, modern Python code with type hints - Apply the Markov Clustering (MCL) algorithm to partition complex networks of matching records - Fine-tune clustering parameters like inflation to control cluster granularity and eliminate false positives - Evaluate the accuracy of your entity resolution results using standard data quality metrics - Structure your data pipelines efficiently using modern Python data-handling libraries You will begin by mastering essential terminology and the mathematical intuition behind Markov processes before moving step-by-step through practical implementation, parameter tuning, and validation strategies. This course is designed for beginner data analysts, software developers, and database engineers who want to solve data deduplication problems. No advanced prior knowledge of graph theory or machine learning is required. Start reading today to unlock cleaner, more reliable data systems.