Machine Learning & AI Foundations: Linear Regression

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Created by: Keith McCormick

Produced in 2018

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Course Description

Having a solid understanding of linear regression - a method of modeling the relationship between one dependent variable and one to several other variables - can help you solve a multitude of real-world problems. Applications areas involve predicting virtually any numeric value including housing values, customer spend, and stock prices. This course reveals the concepts behind the most important linear regression techniques and how to use them effectively. Throughout the course, instructor Keith McCormick uses IBM SPSS Statistics as he walks through each concept, so some exposure to that software is assumed. But the emphasis will be on understanding the concepts and not the mechanics of the software. SPSS users will have the added benefit of being exposed to virtually every regression feature in SPSS.

Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.

Intermediate Level

Time: 3h 57m

Instructor Details

Keith McCormick

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Keith McCormick is an independent data miner, trainer, speaker, and author.

Keith is skilled at explaining complex methods to new users or decision makers at many levels of technical detail. He specializes in predictive models and segmentation analysis including classification trees, neural nets, general linear model, cluster analysis, and association rules.