BigQuery ML for Data Analysts: Practical Machine Learning with SQL

Data analysts often need predictive power but struggle with the complexity of setting up external machine learning pipelines and moving massive datasets. BigQuery ML solves this by allowing you to build and run machine learning models directly where your data lives, using standard SQL. This text-based course helps you transition from basic data querying to building predictive models, enabling you to generate valuable insights and forecasts without leaving your data warehouse. What you'll learn: - Understand the fundamental concepts of SQL-based machine learning and key terminology. - Create and train supervised machine learning models, including regression and classification, using BigQuery SQL. - Evaluate model performance using built-in metrics to ensure accurate predictions. - Generate predictions and perform batch inference directly on your analytical datasets. - Manage and monitor your deployed models within the database environment. - Explore modern integration patterns, including connecting to external foundation models for text generation and analysis. You will start with foundational machine learning definitions and basic concepts before moving step-by-step through practical SQL examples. The written material guides you through selecting the right model, writing training queries, interpreting evaluation results, and generating predictions. This course is designed for data analysts, database administrators, and SQL users who want to add predictive analytics to their skillset. No prior machine learning or Python experience is required. Start reading today to unlock the predictive power of your SQL queries.