Bag of Words & TF-IDF

Summary

Simple ways to turn text into numeric vectors by counting word occurrences (BoW) or weighting by importance across a corpus (TF-IDF) — the classic baseline before embeddings.

How to Learn This

1Build a BoW and a TF-IDF representation of a small text corpus with scikit-learn.
2Train a simple classifier (Naive Bayes, logistic regression) on TF-IDF features.
3Learn why TF-IDF down-weights common words and up-weights distinctive ones.

Text Preprocessing (Tokenization, Stemming, Lemmatization)

Word Embeddings (Word2Vec, GloVe)

More topics in Natural Language Processing

Text Preprocessing (Tokenization, Stemming, Lemmatization)Word Embeddings (Word2Vec, GloVe)Named Entity Recognition Sentiment Analysis Hugging Face Transformers Library Pretrained NLP Models (BERT, RoBERTa)Sequence Classification Text Summarization Question Answering Models

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