• Online, Self-Paced
Course Description

Amazon SageMaker provides broad-set capabilities for machine learning (ML) as it helps to prepare, train, and quickly deploy ML models. Use this course to learn more about the basic capabilities of SageMaker and work with it to implement solutions to various machine learning problems.

Explore features and functionalities of SageMaker through practical demos and discover how to implement hyperparameter tuning. This course will also help you explore algorithms in SageMaker, such as linear learner, XGBoost, object detection, and semantic segmentation.

After completing this course, you'll be able to train and tune a range of algorithms in order to solve simple classification tasks for natural language processing (NLP) and computer vision.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture

Feedback

If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.