Machine Learning Fundamentals Linear Regression
About
An introduction to Linear Regression: what it is, how it works, and how to run a model using scikit-learn.
Learning Objectives
You will be able to:
- Describe statistical modeling and machine learning.
- Describe the principles of linear regression.
- Interpret the coefficients of a linear regression model.
- State the assumptions of a linear regression model.
- Fit, evaluate, and generate predictions from a linear regression model in
scikit-learn. - Identify the difference between simple and multiple linear regression.
- Include categorical variables in a linear regression model.
Content
| Topic | About |
|---|---|
| Full Lesson Deck | An introduction to Linear Regression |
| 02 Simple Linear Regression | A guided walk through of how to build a simple linear regression model |
| 03 Multiple Linear Regression | A guided walk through of how to build a multiple linear regression model |
| Practice | An optional additional exercise to help practice building linear regression models |
Prerequisites
- Interpret and use Python dictionaries
- Build
pandasDataFramesfrom dictionaries - Perform simple data manipulation on
pandasobjects - Build
forandwhileloops in Python - Use
pip installfor package management
References
📖 Reference Materials
Important note
Please ignore the _layouts folder and the config.yml file in the root of this repo. They are needed to make sure the repo renders well; please don’t change them or delete them and your repo will work fine :)