Introduction to Linear Modeling in Python

Description

In this course, you will :

• Focus on the parameters slope and intercept, how they define the model, and how to interpret them in a variety of applications.
• We investigate some of the most common prediction flaws and limitations, and we evaluate and compare models by quantifying and contrasting several measures of goodness-of-fit, such as RMSE and R-squared.
• introduce inferential statistics concepts and apply them to investigate how maximum likelihood estimation and bootstrap resampling can be used to estimate linear model parameters.

Syllabus :

• Exploring Linear Trends
• Building Linear Models
• Making Model Predictions
• Estimating Model Parameters