amanjeetsahu/Natural-Language-Processing-Specialization

Covers four courses implementing classical to modern NLP architectures: sentiment analysis via logistic regression and naive Bayes, vector space models with PCA, sequence modeling with GRUs and LSTMs for tasks like named entity recognition and language generation, and transformer-based approaches including encoder-decoder attention for machine translation, T5/BERT for question-answering, and reformer models for dialogue systems. Implementations progress from foundational algorithms like minimum edit distance and n-gram language models through Word2Vec training to advanced techniques like Siamese networks for semantic similarity and attention mechanisms.

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