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