Planning Information
The duration of this course is 4 Days.
Classes are priced from $2,700.00. See individual sessions for details.
Description
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
- Developers
- Solutions architects
- Data engineers
- Anyone who wants to learn about the ML pipeline via Amazon SageMaker, even if you have little to no experience with machine learning
Prerequisites
- Basic knowledge of Python
- Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch)
- Basic understanding of 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
To request private training or future dates, call 610-321-3500 or
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