Applied Machine Learning with BigQuery on Google Cloud

Description

Learn to design, plan, and scale cloud implementations with Google Cloud Platform's BigQuery. This course will walk you through the fundamentals of applied machine learning and BigQuery ML along with its history, architecture, and use cases.

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-BigQuery-on-Google-s-Cloud

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

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.

With the help of bite-sized videos, this course will help you build your own machine learning models at scale using BigQuery.

Get a good introductory grounding in Google Cloud Platform, specific to BigQuery * Understand the history, architecture, and use cases of BigQuery for machine learning engineers * Discover relevant materials and resource files to reinforce your learning

https://github.com/PacktPublishing/Applied-Machine-Learning-with-BigQuery-on-Google-s-Cloud

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.

1. Introduction

2. BigQuery Basics

3. An Introduction to Applied Machine Learning

4. Machine Learning Libraries

5. Classification and Regression

6. Machine Learning with BigQuery