Ad Prediction Systems with Online Learning and Feature Engineering

Ad prediction is the backbone of digital advertising, requiring systems that can learn from user behavior in real time. Understanding how to process streaming data and update models continuously is a highly sought-after skill in modern machine learning engineering. In this course, you will learn how to design, build, and evaluate ad prediction systems. You will transition from understanding basic click-through rate concepts to implementing online learning algorithms and engineering high-impact features for dynamic ad sets. What you'll learn: - Understand the foundational mechanics of digital advertising auctions and click-through rate prediction. - Implement online learning algorithms to update models continuously as new user interaction data arrives. - Engineer robust features using tree-based models and neural networks to capture complex user-ad interactions. - Apply logistic regression optimized for massive, sparse datasets common in advertising systems. - Evaluate model performance using modern calibration techniques and streaming evaluation metrics. - Handle class imbalance and high-cardinality categorical features using modern hashing tricks. Starting with key definitions and fundamental ad tech concepts, you will progress through clear written explanations and practical code snippets, moving from basic logistic regression to advanced feature generation and real-time model updates. This course is designed for beginners, aspiring data scientists, and software engineers looking to enter the ad tech space, with no prior ad prediction experience required. Start reading today to master the core engineering principles behind modern ad prediction systems.