Feature Engineering for Feed Engagement Prediction

Building recommendation systems and feed ranking algorithms requires more than just raw data; it demands high-quality features that capture real user behavior. This text-based course guides you through the process of designing, transforming, and selecting features specifically for predicting feed engagement. You will transition from working with messy raw social media data to structuring high-performing feature sets. By studying the mechanics of user-author relationships, text metadata, and contextual signals, you will learn how to prepare datasets that significantly improve machine learning model performance. What you'll learn: - Understand the foundational concepts of feature engineering and how engagement prediction models work. - Design user-author interaction features to capture historical relationship strength and affinity. - Extract meaningful signals from text content using basic NLP techniques and modern text representation patterns. - Configure contextual and temporal features, such as device types, time of day, and trending topics. - Apply techniques to prevent data leakage and manage training-serving skew in predictive pipelines. - Explore the basics of modern feature stores and how they streamline feature management in production. The course begins with essential terminology and the theory of engagement prediction before guiding you through structured written explanations of feature extraction, transformation, and selection. You will read through clear code snippets and conceptual breakdowns to build a solid foundation in feature engineering workflows. This course is designed for aspiring data scientists, machine learning beginners, and software engineers looking to understand recommendation system data pipelines. No advanced machine learning background is required. Start reading today to master the art of transforming raw social media interactions into powerful predictive features.