Bayes' theorem
→ Spam filters. Medical diagnostics. Any case where you update the probability after receiving new data.
OLS loss function (sum of squared errors)
→ Linear regression. Housing price forecasting. We minimize "how much we've missed the mark".
Entropy
→ Decision trees. Information gain. A measure of how "mixed up" the classes/data are.
Normal distribution
→ A/B tests. Confidence intervals. The assumption that most values cluster around the mean.
F1-score
→ Unbalanced datasets. Fraud/scams. When accuracy misleads and gives a false sense of quality.
Sigmoid
→ Logistic regression. Neural network outputs. It converts any number into a probability.
Know the formula. Know when to apply it.
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