BigQuery ML Foundations: Machine Learning and Inference with SQL

Many data analysts want to leverage machine learning but struggle with complex programming pipelines and moving massive datasets out of their data warehouses. BigQuery ML solves this by allowing you to build and execute machine learning models directly where your data lives using standard SQL. In this text-based course, you will transition from standard data analysis to predictive analytics. You will understand how to train models, evaluate performance, and run real-time inferences without leaving your database environment. What you'll learn: Understand the core concepts of machine learning and how database-centric ML works; Write SQL queries to train regression, classification, and forecasting models; Execute model inferences and predictions directly on large-scale datasets; Evaluate model performance metrics using built-in SQL evaluation functions; Integrate modern generative AI and large language models using remote models in BigQuery; Apply best practices for structuring data and managing ML workflows inside the data warehouse. The course begins with foundational machine learning terminology and database setup. You will then progress through written guides and SQL examples that demonstrate how to construct, evaluate, and deploy predictive models using familiar database queries. This course is designed for beginner data analysts, database administrators, and SQL developers who want to learn machine learning. No prior Python, R, or advanced machine learning experience is required—only a basic understanding of SQL. Start your journey into database-driven machine learning today.