BigQuery ML: Machine Learning and Inference for Data Analysts

Data analysts often face technical barriers when transitioning from querying data to building machine learning workflows. BigQuery ML bridges this gap by allowing you to develop, train, and run predictive models directly within your data warehouse using familiar SQL syntax. This text-based course guides you through the process of turning static database queries into dynamic predictive insights. By completing this course, you will transition from basic data querying to performing advanced database-driven inference. You will understand how to leverage built-in machine learning capabilities to generate predictions, analyze trends, and integrate modern generative AI models without the need to export your data to external environments. What you'll learn: - Understand the core concepts of machine learning inference and database-centric workflows - Create and train supervised and unsupervised ML models using standard SQL queries - Evaluate model performance and interpret key metrics directly in the console - Apply regression, classification, and forecasting models to real-world datasets - Integrate generative AI and large language models for advanced text inference inside BigQuery - Manage, monitor, and deploy your models for continuous database-driven predictions This course begins with foundational definitions of machine learning and database inference before progressing to practical SQL-based modeling scenarios. You will read structured explanations, analyze query patterns, and practice writing your own machine learning SQL commands through written exercises. This course is designed for data analysts, database administrators, and beginners curious about database-integrated machine learning. A basic familiarity with standard SQL queries is recommended, but no prior background in Python or advanced programming is required. Start reading today to unlock the predictive power of your data warehouse.