Course description

This course explores how to the use of the iterative machine learning (ML) process pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the process 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. Learners with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.

Course level: Intermediate

Duration 4 days

Activities

This course includes presentations, group exercises, demonstrations, and hands-on labs

Intended audience

This course is intended for:

• Developers
• Solutions Architects
• Data Engineers
• Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker

Prerequisites

We recommend that attendees of this course have:

• Basic knowledge of Python programming language
• Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
• Basic experience working in a Jupyter notebook environment

Course Outline

• Module 1: Introduction to Machine Learning and the ML Pipeline
• Module 2: Introduction to Amazon SageMaker
• Module 3: Problem Formulation
• Module 4: Preprocessing
• Module 5: Model Training
• Module 6: Model Evaluation
• Module 7: Feature Engineering and Model Tuning
• Module 8: Deployment