Multiple Linear Regression in Python

4 hours of instruction

Multiple linear regression is a supervised learning technique that allows analysts to model the relationship between a certain number of labeled features and a single continuous numerical target variable. In this course, learners will encounter the mathematical underpinnings of regression models in general before building, optimizing, and evaluating a multiple linear regression model in Python. Learners will also discuss concepts such as statistical significance to clearly present their findings.

OBJECTIVES

1. Understand the theory behind Multiple Linear Regression and identify its use cases
2. Build, optimize and evaluate these multiple regression models
3. Assess statistical significance and validate models for explanatory power and bias
4. Cover techniques used to identify influential points and correlated variables

PREREQUISITES

Moderate familiarity wrangling and analyzing data using simple linear regression in Python, is required.

SYLLABUS & TOPICS COVERED

1. Multiple Linear Regression
   • Multiple linear regression definition and use cases
   • Theory behind multiple linear regression
   • Multiple linear regression implementation on a dataset

SOFTWARE REQUIREMENTS

You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.