I'd recommend following a course to get an idea of what's involved. The Stanford course[1,2] goes deep into the neural network side of things; although lots of

I'd recommend following a course to get an idea of what's involved. The Stanford course[1,2] goes deep into the neural network side of things; although lots of state of the art systems use these, I'd suggest that starting by thinking about language and what makes it difficult to process (with the help of some (computational) linguistics) is also helpful. Knowledge of e.g. morphology may help you make decisions down the line with e.g. stemming for word embeddings. The Cambridge course[3] gives an introduction in this area (disclosure: I TA this). Aside from that, yes. Lots, but not all, of NLP these days involves ML in some form. Most things I've seen are done in Python, what with all the NLP-specific and general ML libraries available. In terms of textbooks, Speech & Language Processing by Jurafsky & Martin is great. Might not be the best way of diving in, but it's a good resource to go deeper into things. The draft third edition, while only partially complete, is online for free [4]. [1] http://web.stanford.edu/class/cs224n/ http://web.stanford.edu/class/cs224n/ [2] https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6 https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_... [3] https://www.cl.cam.ac.uk/teaching/1819/NLP/ https://www.cl.cam.ac.uk/teaching/1819/NLP/ [4] https://web.stanford.edu/~jurafsky/slp3/ https://web.stanford.edu/~jurafsky/slp3/