• Online, Self-Paced
Course Description

Both supervised and unsupervised machine learning techniques are at the forefront of the predictive analytics and data mining industry. Discover machine learning features and tools, and explore propensity scoring and segmentation modeling.

Learning Objectives

Machine Learning

  • start the course
  • identify key features of machine learning
  • identify key tools used for machine learning and the high-level process steps
  • identify key features of deep learning
  • distinguish between supervised and nonsupervised learning methods
  • identify key features of ensemble techniques
  • measure ensemble error rate

Propensity Score and Segmentation Modeling

  • recognize key features of the propensity score
  • identify key features of propensity score matching
  • estimate treatment effects
  • apply propensity score matching
  • identify key features of segmentation modeling
  • distinguish between exploratory data analysis and cluster segmentations

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.