The 15-Day AI/ML Interview Bootcamp
Day 7 — Naive Bayes and your first text classifier
Bayes' theorem in plain words, the naive independence assumption and why it works anyway, tokenisation, bag of words, TF-IDF, Laplace smoothing, and a spam classifier built from scratch — the probability and NLP basics every machine learning interview tests before you talk about transformers.
This morning you texted "are you free tonight? call me to confirm" and it landed in a friend's inbox. Somewhere else a script sent ten thousand copies of "CONGRATULATIONS you have won, call now to claim" and not one of them landed anywhere. A program read both, knew nothing about you or the script, and sorted them in less time than it took you to hit send. Then, in the interview: "Explain Bayes' theorem with an example. Then tell me why Naive Bayes is naive." You remember there's a P(A | B) in it somewhere, and that the word "prior" comes up, and that's where it stops. Today you and I build th…
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