NLP with Python — Hands-On
Build real NLP applications using Python, Hugging Face Transformers, spaCy, and NLTK with practical code examples and best practices.
NLP Libraries & Tools
- Hugging Face Transformers: The #1 library for modern NLP — 200K+ pre-trained models, simple pipeline API, supports PyTorch and TensorFlow
- spaCy: Industrial-strength NLP — tokenization, NER, POS tagging, dependency parsing, text classification. Fast and production-ready
- NLTK: Educational NLP toolkit — comprehensive but slower. Good for learning fundamentals like stemming, tokenization, and corpus analysis
- LangChain: Framework for building LLM applications — chains, agents, memory, retrieval. Connect LLMs to tools and data
- Sentence-Transformers: Create semantic embeddings for similarity search, clustering, and retrieval. Powers many RAG systems
- Gensim: Topic modeling and word embeddings — LDA, Word2Vec, Doc2Vec training and inference
Practical NLP Pipeline Example
Building a sentiment analysis pipeline: 1) Data Collection — scrape reviews or use datasets like IMDB, Yelp, or Amazon reviews. 2) Preprocessing — tokenize, clean, handle emojis and slang. 3) Model Selection — for quick results use Hugging Face pipeline('sentiment-analysis'), for custom data fine-tune DistilBERT. 4) Fine-tuning — load pre-trained model, add classification head, train on your labeled data (typically 1000+ examples). 5) Evaluation — measure accuracy, F1-score, precision, recall on a held-out test set. 6) Deployment — export model with ONNX for fast inference, serve via FastAPI, monitor predictions in production. The entire pipeline from data to deployment can be built in under 100 lines of Python code using Hugging Face.