Natural Language Processing (NLP) in Python - From Zero to Hero
summary
Covers concrete recipes: build a classifier with CountVectorizer + linear SVM and see how binary versus counts and n‑grams change results, replace that with spaCy word vectors to generalize (story → books) and remove OOV failure, use NLTK for stemming/lemmatization and stop words, use regex for pattern matching, try TextBlob for POS and sentiment, then load transformers and show BERT fine‑tuning for contextual sense (writes a check ≠ check out the book) and a baseline Amazon-review classifier hitting about 65%.
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