• Online, Self-Paced
Course Description

Unsupervised learning can provide powerful insights on data without the need to annotate examples. In this course, you will learn several different techniques in machine learning.

Learning Objectives

Introducing Unsupervised Learning

  • start the course
  • describe unsupervised learning and some of the problems it can solve

Rule Association

  • describe rule association and how the apriori algorithm performs this task
  • use the apriori algorithm for rule association in Python

Cluster Analysis

  • describe clustering and the types of problems it applies to
  • describe the k-means clustering algorithm
  • use SciKit Learn to build clusters in python

Anomaly Detection

  • describe anomaly detection, the types of problems solved with anomaly detection, and some approaches to anomaly detection
  • use scikit learn to perform anomaly detection

Dimensionality Reduction

  • describe the problems with dimensionality and why efforts to reduce dimensionality should be taken
  • describe principal component analysis for dimensionality reduction
  • use SciKit Learn to perform dimensionality reduction

Practice: Unsupervised Learning

  • perform dimensionality reduction and clustering tasks in Python

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.