• Online, Self-Paced
Course Description

Machine learning is a particular area of data science that uses techniques to create models from data without being explicitly programmed. In this course, you'll explore the conceptual elements of various machine learning techniques.

Learning Objectives

Machine Learning Introduction

  • start the course
  • identify problems in which supervised learning techniques apply
  • identify problems in which unsupervised learning techniques apply
  • apply linear regression to machine learning problems
  • identify predictors in machine learning

Regression and Classification

  • apply logistic regression to machine learning problems
  • describe the use of dummy variables
  • use naive bayes classification techniques
  • work with decision trees

Clustering

  • describe K-means clustering
  • define cluster validation
  • define principal component analysis

Errors and Validation

  • describe machine learning errors
  • describe underfitting
  • describe overfitting
  • apply k-folds cross validation
  • describe fall-forward and back-propagation in neural networks
  • describe SVMs and their use

Practice: Choosing a Method

  • choose the appropriate machine learning method for the given example problems

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.