Welcome to Applied Machine Learning with BigQuery on Google’s Cloud.
Right now, applied machine learning is one of the most in–demand career fields in the world, and will continue to be for some time. Most of applied machine learning is supervised. That means models are built against existing datasets.
Most real–world machine learning models are built in the cloud or on large on–prem boxes. In the real–world, we don’t built models on laptops or on desktop computers.
Google Cloud Platform’s BigQuery is a serverless, petabyte–scale data warehouse designed to house structured datasets and enable lightning fast SQL queries. Data scientists and machine learning engineers can easily move their large datasets to BigQuery without having to worry about scale or administration, so you can focus on the tasks that really matter generating powerful analysis and insights.
In this course, you ll:
Get an introduction to BigQuery ML.
Get a good introductory grounding in Google Cloud Platform, specific to BigQuery.
Learn the basics of applied machine learning.
Understand the history, architecture and use cases of BigQuery for machine learning engineers.
Learn how to building your own machine learning models at scale using BigQuery.
This is a mid–level course and basic experience with SQL and Python will help you get the most out of this course.
Specification: Applied Machine Learning with BigQuery on Google’s Cloud