Скачать с ютуб NLP Demystified 8: Text Classification With Naive Bayes (+ precision and recall) в хорошем качестве

NLP Demystified 8: Text Classification With Naive Bayes (+ precision and recall)

Course playlist:    • Natural Language Processing Demystified   In this module, we'll apply everything we've learned so far to a core task in NLP: text classification. We'll learn: how to derive Bayes' theorem how the Naive Bayes classifier works under the hood how to train a Naive Bayes classifier in scikit-learn and along the way, deal with issues that come up. how things can go wrong when using accuracy for evaluation precision, recall, and using a confusion matrix In the demo, we'll apply everything from the slides to build a full text classifier with spaCy and scikit-learn. We'll go from a bunch of raw text, preprocess and vectorize it, and build multiple versions of our text classifier, improving it each iteration. Colab notebook: https://colab.research.google.com/git... Timestamps: 00:00:00 Naive Bayes 00:00:25 Classification as a core task in NLP 00:01:11 Revisiting conditional probability 00:03:26 Deriving Bayes' Theorem 00:04:12 The parts of Bayes' Theorem 00:05:43 A spatial example using Bayes' Theorem 00:07:33 Bayes' Theorem applied to text classification 00:08:30 The "naive" in Naive Bayes 00:09:34 The need to work in log space 00:10:05 Naive Bayes training and usage 00:13:27 How the "accuracy" metric can go wrong 00:14:10 Precision, Recall, and Confusion Matrix 00:17:47 DEMO: Training and using a Naive Bayes classifier 00:36:28 Naive Bayes recap and other classification models This video is part of Natural Language Processing Demystified --a free, accessible course on NLP. Visit https://www.nlpdemystified.org/ to learn more.