• 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 NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • All-Source Analysis
  • Data Administration
  • Systems Analysis