• Online, Self-Paced
Course Description

Choosing the appropriate technique to deliver confident predictions can be challenging for analysts. Explore algorithms used for predictive analytics, including the K-Nearest Neighbor (k-NN) algorithm and artificial neural network modeling.

Learning Objectives

K-Nearest Neighbor (k-NN)

  • start the course
  • recognize features of the k-NN algorithm
  • recognize distance and weighted distance measures
  • recognize proximity measures for non-numeric attributes
  • implement the k-NN algorithm

Artificial Neural Networks

  • identify key features of artificial neural networks
  • recognize steps and considerations to building artificial neural networks
  • recognize the purpose of nonlinear activation functions and methods to find the global minimum SSE
  • recognize important parameters for artificial neural networks
  • implement an artificial neural network

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.