Users Online

· Guests Online: 45

· Members Online: 0

· Total Members: 188
· Newest Member: meenachowdary055

Forum Threads

Newest Threads
No Threads created
Hottest Threads
No Threads created

Latest Articles

· Do while loop
· The for loop in C
· C while loop
· Pointers in C
· Functions in C

Articles Hierarchy

Articles Home » Artificial Intelligence and Machine Learning » Machine Learning & AI Foundations: Linear Regression

Machine Learning & AI Foundations: Linear Regression

Machine Learning & AI Foundations: Linear Regression

Created by Keith McCormick


01_001.Welcome

Machine Learning & AI Foundations: Linear Regression with Keith McCormick

3h 57m • COURSE
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.
Topics include:
  • Building effective scatter plots in Chart Builder
  • Challenges and assumptions of multiple regression
  • Checking assumptions visually
  • Creating dummy codes
  • Creating and testing interaction terms
  • Understanding partial and part correlations
  • Spotting problems and taking corrective action
  • Dealing with multicollinearity
      
Course Contents
01.Introduction 02.Simple Linear Regression 03.Introduction to Multiple Linear Regression 04.Dummy Code and Interaction Terms 05.Three Regression Strategies 06.Spotting Problems and Taking Corrective Action 07.Other Approaches to Regression 08.Conclusion Ex_Files_MachineLearning_AI_Linear_Regression

· Posted by Superadmin on May 14 2023 · 0 Comments · 189 Reads · Print
Page 1 of 50: 1234...50

Comments

No Comments have been Posted.

Post Comment

Please Login to Post a Comment.

Ratings

Rating is available to Members only.

Please login or register to vote.
No Ratings have been Posted.
Render time: 0.73 seconds
10,819,426 unique visits