This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem.
Intended Audience
This course is intended for:
Delivery Method
This course is delivered through a mix of:
We recommend that attendees of this course have the following prerequisites:
In this course, you will learn how to:
Module 1: Introduction to Machine Learning and the ML Pipeline
Module 2: Introduction to Amazon SageMaker
Module 3: Problem Formulation
Day Two
Recap and Checkpoint #1
Module 4: Preprocessing
Module 5: Model Training
Day Three
Recap and Checkpoint #2
Module 6: Model Training
Module 7: Feature Engineering and Model Tuning
Day Four
Lab 4: Feature Engineering (including project work)
Recap and Checkpoint #3
Module 8: Module Deployment
Module 9: Course Wrap-Up
Join our public courses in our Germany facilities. Private class trainings will be organized at the location of your preference, according to your schedule.