This 4-day 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. Learner with little to no machine learning experience or knowledge will benefit from this course. Basic knowledge of Statistics will be helpful.
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
Du lundi au vendredi, de 8:30 à 18:00.
Monday to Friday, from 8:30 am to 06:00 pm.
