NLP enables computers to understand, interpret, and generate human language. Python is the dominant language for NLP with extensive libraries.
Essential libraries: NLTK for learning fundamentals, spaCy for production NLP, Hugging Face Transformers for state-of-the-art models, and gensim for topic modeling.
Core NLP tasks: tokenization (split text into words/sentences), named entity recognition (extract names, dates, locations), part-of-speech tagging, and dependency parsing.
Text preprocessing: lowercasing, stop word removal, stemming/lemmatization, and removing special characters. These steps improve model accuracy.
Sentiment analysis: classify text as positive, negative, or neutral. Use pre-trained models from Hugging Face or train custom classifiers.
Text embeddings convert words to vectors for similarity search. Word2Vec, GloVe, and sentence-transformers create meaningful representations.
Practical applications: spam detection, content classification, document summarization, named entity extraction, and text generation.