• Online, Self-Paced
Course Description

Applying machine learning to problems can be a difficult tasks because of all the different models that are offered. In this course you will learn how to evaluate and select machine learning models and apply machine learning to a problem.

Learning Objectives

Model Evaluation and Selection

  • start the course
  • describe the two main types of error in machine learning models and the tradeoff between them
  • describe how to use cross-validation to show how generalized a model is
  • describe cross-validation in Python to obtain strong evaluation scores
  • describe different metrics that can be used to evaluate binary classification models
  • describe different metrics that can be used to evaluate non-binary classification models
  • describe common evaluation metrics for evaluating classification models
  • describe different metrics that can be used to evaluate regression models
  • describe how to use Python to calculate common evaluation methods

Machine Learning With AWS

  • describe AWS machine learning
  • set up an AWS environment and import data sources
  • create a model with AWS
  • set training criteria with AWS and train a model

Practice: Bias and Variance

  • define bias, variance, and tradeoffs

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.

Feedback

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