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Module · 8 lessons
Regression
Predict continuous values with linear regression and understand the gradient descent that trains it.
At a glance
Level
Intermediate
Lessons
8 lessons
Time to complete
2–3 weeks
Cost
Free forever · no sign-up
Regression is how machine learning predicts continuous numbers — prices, temperatures, demand. In this module you will build, interpret, and validate linear regression models, then open the hood to understand gradient descent, the optimization engine that trains them.
Lessons in this module
1
Introduction to Linear Regression
Learn linear regression end to end by predicting real car prices with scikit-learn.
2
Interpreting Regression Parameters
Fit linear regression with scikit-learn and read what its intercept and coefficients actually mean
3
Checking Linear Regression Fit
Diagnose linear regression quality with residual plots, RMSE, and the R-squared score
4
Applying Linear Regression Models
Apply linear regression end to end: features, scaling, test error, and honest evaluation
5
Understanding Gradient Descent
Understand gradient descent: the optimizer that finds the best regression line by stepping downhill.
6
Implementing Gradient Descent in Python
Build gradient descent from scratch in Python and watch the loss fall
7
Gradient Descent with Scikit-Learn
Scale gradient descent to real data with scikit-learn's SGDRegressor on car prices
8
Guided Project: Predicting Insurance Costs
Build and evaluate a real linear model that predicts medical insurance costs
Achievement
Complete all 8 lessons to finish the Regression module.