SQL-Based Machine Learning and Inference with BigQuery

You do not need to master complex Python libraries or manage external infrastructure to leverage the power of machine learning. By using BigQuery ML, data analysts can build and execute predictive models directly where their data lives using familiar SQL syntax. This text-based course guides you from foundational database concepts to deploying practical machine learning models for inference. You will learn to prepare your data, select the right algorithms, and generate predictions to drive business decisions, all within a secure and scalable cloud data warehouse environment. What you'll learn: Understand the core concepts of in-database machine learning and inference; Write SQL queries to train regression, classification, and forecasting models; Evaluate model performance using standard metrics directly inside the warehouse; Generate batch predictions and real-time inferences on large datasets; Integrate modern generative AI and LLM capabilities using remote models in BigQuery; Apply best practices for data preparation, feature selection, and model management. We begin with the absolute basics of database-centric machine learning before moving into step-by-step SQL tutorials for building, evaluating, and running inference on diverse models. You will read clear explanations and analyze real-world SQL code snippets designed to make complex concepts intuitive. This course is designed for beginner data analysts, SQL developers, and business intelligence professionals who want to transition into predictive analytics without a deep background in advanced programming. Start reading today and transform your SQL queries into powerful predictive tools.