This course gives you a complete, hands-on introduction to Natural Language Processing (NLP) — the field that teaches machines to understand and process human language. You’ll start with the fundamentals, exploring how text data is prepared, represented, and used to build intelligent language models.

You’ll learn everything from text preprocessing and feature extraction using Bag of Words, TF-IDF, and N-grams, to advanced embedding techniques like Word2Vec with CBOW and Skip-gram architectures. The course walks you through building text classification, POS tagging, and sequence models using algorithms such as the Viterbi and Hidden Markov Models.

Finally, you’ll bring it all together in a real-world NLP project, where you develop a model to detect duplicate question pairs (like Quora’s question similarity task) and deploy it end-to-end on Heroku.

What you will learn:

• NLP pipeline and text preprocessing
• Text representation using Bag of Words, TF-IDF, and N-grams
• Word embeddings with Word2Vec (CBOW and Skip-gram)
• Text classification using vector-based features
• POS tagging and sequence models (HMM, Viterbi)
• Building and deploying real-world NLP projects