Natural Language Processing with Sequence Models
DeepLearning.AI · View on Coursera ↗
Taught by Younes Bensouda Mourri, Łukasz Kaiser, Eddy Shyu
Data ScienceComputer Science
About the course
In Course 3 of the Natural Language Processing Specialization, you will: a) Train a neural network with word embeddings to perform sentiment analysis of tweets, b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model, c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the s
The dissent
The most-upvoted critical review (43 votes, 1.0★).
“Well, very weak and oversimplified course. All videos are indecently short (from 1 to 4 minutes in majority) and do not give any intuition or understanding of the sequence models. GRU's and LSTM's are explained too briefly. Weird decision to choose Trax framework, it offers no reasonable advantages over Keras in this course. Also, I do not think that implementing the data generator function in the programming assignments gives anyone better intuition in understanding the core material. The last two assignments can be completed even without watching the lecture videos. To sum up, this course can be valuable as just the short intro to recurrent-based networks, but do not expect to deepen your knowledge.”
Coursera's 4.5 comes from 1,188 ratings. Of those, 6.0% are 1–2★.
Starisk reads the reviews learners voted most helpful, and weights each by the square root of its votes: a review with 80 upvotes counts about five times one with 2. Unvoted reviews are not skipped out of laziness — by this model they carry no evidence that other students endorsed them.
That measures what the most-endorsed reviews say, not “true quality.” The rating distribution above is the whole population, so you can see the sample next to it.
Reviews read on 23 Oct 2026.Read them on Coursera ↗