Maximum Likelihood, Entropy and Cross-Entropy Explained From Scratch

Why does almost every classifier in machine learning minimize cross-entropy? Because it is secretly maximum likelihood in disguise. In this episode of Machine Learning from Zero to Advanced, we build everything by hand with real numbers: likelihood versus probability, deriving the maximum likelihood estimate for a coin and for a Gaussian mean, why we take logs, floating-point underflow, a preview of MAP estimation, entropy as surprise, cross-entropy, KL divergence, and the proof that minimizing cross-entropy equals maximizing likelihood. We finish with short Python snippets you can run yourself. Next episode: loss functions and gradient descent. Subscribe so you do not miss it. 0:00 Intro 0:53 Likelihood vs Probability 2:28 MLE for a Coin 4:03 MLE for a Gaussian 6:00 Why We Use Logs 7:43 MLE vs Bayesian View 9:02 Entropy as Surprise 10:47 Cross-Entropy and KL 12:27 Cross-Entropy Equals MLE 14:08 Python and Recap #MachineLearning #MaximumLikelihood #CrossEntropy #Entropy #KLDivergence