Natural Language Processing with Probabilistic Models
DeepLearning.AI · View on Coursera ↗
Taught by Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu
Data ScienceComputer Science
About the course
In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. By the end of
The dissent
The most-upvoted critical review (18 votes, 1.0★).
“It is the worst course on deeplearning.ai ever. It is too simple for those who already took DL specialization and too difficult for new ones. The 'lectures' are too superficial and you will barely understand a thing. But tasks are huge -- a lot of spaghetti code with few levels of enclosed IF's, with constructions like A[i][j:k][l+1]. You will spend your time doing the bad implementation of 100K times implemented things that will not provide you with enlightenment on how they are implemented because of a lack of the theory. And nobody will teach you to use standard tools on simple and understandable examples.”
Coursera's 4.7 comes from 1,791 ratings. Of those, 2.2% are 1–2★.
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Reviews read on 23 Oct 2026.Read them on Coursera ↗