In this intermediate-level course, you will learn how to solve a real-world use case with Machine Learning (ML) and produce actionable results using Amazon SageMaker. This course walks through the stages of a typical data science process for Machine Learning from analyzing and visualizing a dataset to preparing the data, and feature engineering. Individuals will also learn practical aspects of model building, training, tuning, and deployment with Amazon SageMaker. Real life use case includes customer retention analysis to inform customer loyalty programs.
Private in-house trainingApart 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 how to:
We recommend the following prerequisites for attendees of this course:
This course is intended for:
This course will be delivered through a mix of:
This course will cover the following concepts:
Module 1: Introduction to machine learning
Module 2: Introduction to data prep and SageMaker
Module 3: Problem formulation and dataset preparation
Module 4: Data analysis and visualization
Module 5: Training and evaluating a model
Module 6: Automatically tune a model
Module 7: Deployment / production readiness
Module 8: Relative cost of errors
Module 9: Amazon SageMaker architecture and features