course-nlp

Why we included this project

Most NLP tutorials either stop at classical text methods or jump straight into neural nets, leaving you to connect the two yourself. This repo holds the notebooks and lecture material from a University of San Francisco graduate course taught in Python, and it walks the whole arc in one place: topic modeling with NMF and SVD, sentiment classification with Naive Bayes and n-grams, then language models, RNN-based translation, and Transformers. The teaching style is deliberately top-down, so you run working examples before you fully understand the internals, which suits people who learn better by doing than by reading theory first. There's also a chapter on bias and ethics in word embeddings, something most code-heavy tutorials leave out. If you're a self-taught developer or a team building NLP skills and want a structured, free curriculum with runnable notebooks, this is a strong starting point.