Training Data Generation for User Engagement Models

Every modern feed and recommendation system relies on high-quality user engagement data to make accurate predictions. However, raw user interactions are often messy, biased, and highly unbalanced, leading to poor model performance if not prepared correctly. This text-based course guides you through the foundational concepts of generating, balancing, and structuring training data from real-world user behaviors like clicks, views, and shares. You will transition from understanding raw event logs to building clean, model-ready datasets that drive precise engagement predictions. What you'll learn: Understand the fundamental concepts of implicit versus explicit user feedback; Process raw event streams into structured features using modern dataframe libraries; Balance skewed engagement datasets to eliminate selection bias in click-through rate models; Apply robust data-splitting techniques to prevent label leakage and over-fitting; Implement automated validation checks to ensure training data quality and consistency; Address modern challenges like tracking data drift and managing feedback loops. You will start with key terminology and foundational concepts of recommendation systems before moving into structured data preparation workflows. Through clear written explanations and structured code snippets, you will learn how to design reliable data pipelines for machine learning. This course is designed for beginner data scientists, software engineers, and machine learning enthusiasts. No advanced background in modeling is required. Start learning today and build the data foundation needed for high-performing engagement models.