Natural-Language-Processing-Specialization

Why we included this project

This repository collects one learner's completed work from the deeplearning.ai NLP Specialization on Coursera, so it works well as a study companion if you are taking that track. The notebooks follow the four courses in order, starting with logistic regression and Naive Bayes for tweet sentiment, then vector space models and word embeddings, and finishing with sequence models with attention covering machine translation, summarization, and question answering. Each weekly notebook sits next to its lecture slides, which saves you from digging through the course platform's scattered files. Because the material is filed by course and week, it is easy to check your own solutions against it or jump straight to a specific technique like the Viterbi algorithm, GRU text generation, or Siamese networks. Just keep in mind this is a fellow student's study notes, not the official curriculum, so treat it as a supplement rather than a substitute for the course itself.