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.
Private in-house training
Apart from public, instructor-led classes, we also offer private in-house trainings for organizations based on their needs. Call us at +852 2116 3328 or email us at [email protected] for more details.
In this course, you will learn to:
We recommend that attendees of this course have:
This course is intended for:
This course will be delivered through a mix of:
This course will cover the following concepts:
Day 1
Module 0: Introduction
Module 1: Introduction to Machine Learning and the ML Pipeline
Module 2: Introduction to Amazon SageMaker
Module 3: Problem Formulation
Day 2
Checkpoint 1 and Answer Review
Module 4: Preprocessing
Day 3
Checkpoint 2 and Answer Review
Module 5: Model Training
Module 6: Model Evaluation
Day 4
Checkpoint 3 and Answer Review
Module 7: Feature Engineering and Model Tuning
Module 8: Deployment