Machine Learning with Text in scikit learn

on YouTube ↗01h 24m

" summary

Builds and evaluates real SMS spam classifiers from a GitHub tutorial using scikit-learn and pandas, turning text into a sparse document‑term matrix with count vectorizer and insisting on train‑test split before vectorizing. Trains a multinomial Naive Bayes and a logistic regression, reports timing in milliseconds and a surprisingly high 98.8% accuracy (with AU and confusion‑matrix diagnostics), demonstrates how to combine extra numeric features, and explains cross‑validation and probability calibration tradeoffs.

AI-written summary · about

" chapters (8)

1. 0:00Introduction
2. 12:52Agenda and model basics
3. 23:36Text as numerical data
4. 42:16Vectorization and matrices
5. 52:35SMS spam dataset
6. 1:07:59Naive Bayes model
7. 1:21:47Naive Bayes vs logistic regression
8. 1:24:19Conclusion

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