Applied Machine Learning with BigQuery on Google 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 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.

What You Will Learn

• 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

Audience

If you’re interested in building real-world models at scale, using BigQuery, and learning the most used service on GCP, this course is for you. This is a mid-level course, and basic experience with SQL and Python will help you get the most out of this course.

About The Author

Mike West: Mike West is the founder of LogikBot. He has worked with databases for over two decades. He has worked for or consulted with over 50 different companies as a full-time employee or consultant. These were Fortune 500 as well as several small to mid-size companies. Some include Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light, and Northrup Grumman.

Over the last five years, Mike has transitioned to the exciting world of applied machine learning. He is excited to show you what he has learned and help you move into one of the single-most important fields in this space.