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 two business problems: fraud detection, 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 and Python will be helpful.
 Course Objectives  Â
In this course, you learn to:
- Â Select and justify the appropriate ML approach for a given business problem Â
- Â Use the ML pipeline to solve a specific business problem Â
- Â Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Â
- Â Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Â
- Â Apply machine learning to a real-life business problem after the course is complete Â
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Intended Audience
This course is intended for:
- Â Developers Â
- Â Solutions Architects Â
- Â Data Engineers Â
- Â Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Â
 Prerequisites
We recommend that attendees of this course have:
- Â Basic knowledge of Python programming language Â
- Â Basic understanding of AWS Cloud infrastructure (Amazon S3 and Amazon CloudWatch) Â
- Â Basic experience working in a Jupyter notebook environment Â
Course Outline
- Introduction
- Module 1: Introduction to Machine Learning and the ML Pipeline
- Module 2: Introduction to Amazon SageMakerÂ
- 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
- Course wrap-up
