• Online, Self-Paced
Course Description

This 13-video course explores machine learning predictive analytics, and how its application can drive revenues, reduce costs, and provide a competitive advantage to businesses. Learners will observe the predictive modeling process and how to apply tools and techniques for performing predictive analytics, and how to use historical data to identify trends and patterns to forecast future events. First, you will learn about the predicative modeling process, the statistical concepts for predictive modeling, and regression techniques. This course uses two examples to demonstrate commonly used methods of predictive analytics, by examining decision trees and SVMs (support vector machines). Next, you will learn about survival analysis, market basket analysis, and how to apply data for cluster models. You will learn about random forests in predictive analytics, and you will examine probabilistic graphical models. Learn about classification models, and how to organize data into groups based on predicting the class of the data points. Finally, you will explore some best practices for predictive modeling.

Learning Objectives

This 13-video course explores machine learning predictive analytics, and how its application can drive revenues, reduce costs, and provide a competitive advantage to businesses. Learners will observe the predictive modeling process and how to apply tools and techniques for performing predictive analytics, and how to use historical data to identify trends and patterns to forecast future events. First, you will learn about the predicative modeling process, the statistical concepts for predictive modeling, and regression techniques. This course uses two examples to demonstrate commonly used methods of predictive analytics, by examining decision trees and SVMs (support vector machines). Next, you will learn about survival analysis, market basket analysis, and how to apply data for cluster models. You will learn about random forests in predictive analytics, and you will examine probabilistic graphical models. Learn about classification models, and how to organize data into groups based on predicting the class of the data points. Finally, you will explore some best practices for predictive modeling.

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

  • Systems Architecture