Logistic Regression isn’t just about predicting 0s and 1s—it’s a beautiful dance of probability and optimization. At its core lies Maximum Likelihood Estimation (MLE), the technique we use to tune our model’s parameters for the best fit. From Bernoulli assumptions to log-likelihood derivation, from sigmoid curves to regularization—this post walks you through how MLE powers logistic regression, one math step at a time.