Introduction to Natural Language Processing

This course offered by teaches a blend of traditional NLP topics (including regex, SVD, naive bayes, tokenization) and recent neural network approaches (including RNNs, seq2seq, attention, and the transformer architecture), as well as addressing urgent ethical issues, such as bias and disinformation.

Natural Language Understanding (Stanford)

This course is focused on developing systems and algorithms for robust machine understanding of human language.

It draws on theoretical concepts from linguistics, natural language processing, and machine learning.

It also contains special lectures on developing projects, presenting research results, and making connections with industry.

Natural Language Processing | Dan Jurafsky at Stanford University

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