Why Logistic Regression
Binary classification fundamentals, why ordinary least squares linear regression fails on probabilities, and the mathematical necessity of the sigmoid curve.
The Sigmoid and Fitting
Deriving the logistic model from linear regression, mathematical foundations of Binary Cross-Entropy, and optimization via Gradient Descent.
Multiclass and Practical Use
Extending binary logistic regression to multiclass problems via One-vs-Rest and Multinomial Softmax, decision boundaries, and odds-ratio interpretation.