Stanford's introductory natural language processing course, taught by Chris Manning and Dan Jurafsky, covering core NLP techniques such as sequence models, Viterbi decoding, and CRFs. It was offered on Coursera as video lectures with homework assignments.

Commenters describe it as an excellent overview that serves as a good entry point into NLP papers and literature.

What commenters said (2)

1. Strongly recommendsmark_l_watson2019in “Natural Language Processing with Deep Learning”

   Thanks for the link, just subscribed.

   Years ago I took Chris Manning and Dan Jurafsky‘s Coursera NLP class and it was excellent. I also own, I think, every book they have written including Jurafsky’s book on food.

   It is extremely generous of top universities to make their classes available online. Of course, watching the videos and trying the homework assignments on one’s own is not the experience of going to Stanford, but it is less expensive!

2. Recommendsagibsonccc2014in “Convolutional Neural Networks in your browser”

   Sure. Have you looked at the PGM class on coursera for CRFs? The slides are a great condensed version of koller's book on PGMs. NLP is a really easy context they are used in. With that, I would recommend the stanford NLP coursera class (the first one from a few years back) that covers viterbi, CRFS, and general sequential models.I know I'm recommending a lot of MOOC content here, but I would add here that I think they provide a great overview from which to break in to the papers/other literature on the topic.

   For SVMs, outside of the typical research you'd do on wikipedia, I unfortunately haven't had much specific experience with SVMs. I used them quite a bit a few years back for relation extraction and other algorithms, but I'm mainly from an NLP background in that context.

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