A Code-First Introduction to Natural Language Processing
A Code-First Introduction to Natural Language Processing
There have been many major advances in NLP in the last year, and new state-of-the-art results are being achieved every month. NLP is still very much a field in flux, with best practices changing and new standards not yet settled on. This makes for an exciting time to learn NLP. This course covers a blend of more traditional techniques, newer neural net approaches, and urgent issues of bias and disinformation.
Applications covered include topic modeling, classfication (identifying whether the sentiment of a review is postive or negative), language modeling, and translation. The course 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. Topics can be watched in any order. All the code is in Python in Jupyter Notebooks This course was originally taught in the University of San Francisco MS in Data Science program.
Duration: Not defined
Level: Graduate
Certification: No
Cost: Free
Language: English
Type: Self-Paced
Please note: these courses are provided by external sources, links are not actively managed or regularly updated, content might be moved or unavailable.
