Course details

K-Nearest Neighbor (k-NN) & Artificial Neural Networks

K-Nearest Neighbor (k-NN) & Artificial Neural Networks


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.8

Lesson Objectives

K-Nearest Neighbor (k-NN) & Artificial Neural Networks

  • 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
  • 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
  • Course Number:
    df_prma_a09_it_enus

    Expertise Level
    Intermediate