Introduction to Amazon Machine Learning

This course is designed to teach developers, technical marketers, and data scientists how to create and use models with Amazon Machine Learning (Amazon ML). The course covers the basics of machine learning, including data preparation and accessibility for Amazon ML processes. Participants will learn how to evaluate model accuracy, make batch and real-time predictions, and manage environments using tags. Prior to starting the course, learners should have an AWS account and a basic understanding of AWS concepts.

The course offers hands-on experience in building predictive models using real data. It also covers advanced concepts such as tagging and the model lifetime cycle. Participants will learn how to prepare data for Amazon ML and navigate the console, as well as make batch and real-time predictions using Python and the Amazon Machine Learning console. Tools for evaluating and comparing accuracy are also provided.

The instructor for this course is Dan Moore, an AWS expert based in Boulder, Colorado. Dan manages Moore Consulting and specializes in building Amazon Machine Learning models for predicting real estate values and estimating equipment usage. He has been working with AWS since 2008 and holds certifications as a Solutions Architect, AWS Certified Designer, and SysOps administrator.

Overall, this course offers a comprehensive introduction to machine learning with Amazon ML, providing participants with the necessary skills to build and use models effectively.