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 the 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-premises boxes. In the real world, we don’t build 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.
This course covers the basics of applied machine learning and an introduction to BigQuery ML. You will also learn how to build your own machine learning models at scale using BigQuery.
By the end of this course, you will be able to harness the benefits of GCP’s fully managed data warehousing service.
All resources to this course are placed here: https://github.com/PacktPublishing/Applied-Machine-Learning-with-BigQue…
Understand BigQuery specific to machine learning
Learn the basics of Google Cloud Platform, specific to BigQuery
Learn the basics of applied machine learning from a machine learning engineer
Learn how to build machine learning models at scale using BigQuery
Introduction to BigQuery ML
Learn the basics of applied machine learning