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Gaussian Naive Bayes from scratch: per-class stats and log space.

Fit a mean and variance per class per feature, sum log-likelihoods with the log prior, argmax. Why log space, and what conditional independence buys you.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Fit a mean and variance per class per feature, sum log-likelihoods with the log prior, argmax. Why log space, and what conditional independence buys you.

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Next in this trackImplement PCA from scratch via SVD: center the data, project onto top components, report variance.Next in this trackImplement a Gaussian Mixture Model with EM from scratch: E-step responsibilities, M-step updates.Next in this trackSolve linear regression with the normal equations: derive the closed form and implement it stably.

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