Stanford Natural Language Processing Specialization

Used Logistic Regression, Naïve Bayes, and word embeddings to implement sentiment analysis, solve word analogy tasks, and perform word translation.

Applied Dynamic Programming, Hidden Markov Models (HMMs), and word embeddings to build autocorrect, autocomplete, and part-of-speech (POS) tagging systems.

Built and trained Recurrent Neural Networks (RNNs), LSTMs, GRUs, and Siamese Networks for sentiment analysis, text generation, and Named Entity Recognition (NER).

Applied encoder-decoder architectures, causal attention, and self-attention mechanisms to develop Transformer-based models for machine translation, text summarization, and question answering.