What you'll learn

Many workplace decisions depend on predicting whether an event will occur, yet ordinary linear regression has important limitations when applied to binary outcomes. In this course, Logistic Regression for Classification, you’ll gain the ability to frame classification problems, understand how logistic regression produces probabilities, and evaluate how those probabilities support decisions. First, you’ll explore binary and multi-class outcomes, the limits of Ordinary Least Squares (OLS) for binary targets, and the generalized linear model framework connecting linear and logistic regression. Next, you’ll discover how the logit and inverse-logit functions produce probabilities, how coefficients become odds ratios, and how thresholds convert probabilities into class labels. Finally, you’ll learn how to evaluate classification performance with confusion matrices, precision, recall, F1-score, ROC curves, and AUC, and how Poisson regression extends the GLM framework to count outcomes. When you’re finished with this course, you’ll have the skills and knowledge of logistic regression needed to interpret, evaluate, and communicate probability-based classification results in practical settings.