Simple Linear Regression in Python

4 hours of instruction

Regression is a machine learning technique that can be used to model and predict the relationship between variables, features and a continuous numerical target. In this course, learners will identify use cases for simple linear regression, focusing on the relationship between two variables only. Students will build, evaluate, and interpret a simple linear regression model in Python, with an emphasis on checking the model for explanatory and predictive power.

OBJECTIVES

1. Understand the working and identify use cases of simple linear regression models
2. Build and evaluate simple regression models
3. Assess statistical significance and validate these models for explanatory power and bias

PREREQUISITES

Learners must be comfortable using Python to manipulate data and must know how to create basic visualizations.

SYLLABUS & TOPICS COVERED

1. Simple Linear Regression
   • Simple linear regression definition and use cases
   • Theory behind simple linear regression
   • Simple 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.