Course outline
Duration: 4 Days |
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. | |
Audience | This course is intended for:
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Prerequisites | We recommend that attendees of this course have:
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Objectives | In this course, you will learn to:
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Teaching method | Instructor-led training, hands-on labs, demonstrations, and group exercises |
Contents | Module 0: Introduction
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
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