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Module · 5 lessons
Classification
Predict categories with logistic regression and measure classifiers with the right metrics.
At a glance
Level
Intermediate
Lessons
5 lessons
Time to complete
1–2 weeks
Cost
Free forever · no sign-up
Classification predicts categories: spam or not, healthy or at risk, churn or stay. This module teaches logistic regression from the ground up, including the sigmoid function, how to interpret the model, and how to evaluate classifiers with precision, recall, and ROC curves.
Lessons in this module
1
Introduction to Logistic Regression
Learn why classification needs a different model than regression and build your first logistic regression classifier with scikit-learn
2
Interpreting the Regression Parameters
Fit a logistic regression with scikit-learn and learn to read its coefficients as log-odds and odds ratios
3
Evaluating Logistic Regression Models
Go beyond accuracy and evaluate a logistic regression classifier with the confusion matrix, sensitivity, specificity, and precision
4
Applying Logistic Regression Models
Turn a trained logistic regression into real decisions using predicted probabilities, decision thresholds, and the ROC curve
5
Guided Project: Classifying Heart Disease
Apply the full classification workflow end to end by building a logistic regression model that predicts heart disease from real clinical data
Achievement
Complete all 5 lessons to finish the Classification module.