First 2 courses of this specialization were really good. This course was little less than what I expected. Maybe you can keep graded assignments like before.

I've learnt a lot of interesting things during this course.

I subtracted 1 star due to the missing links and notebooks. Please find some time to fix them.

Good. Basic concepts reviewed, some easy-to-understand applications. A little too fast on LSTM and RNNs, the architecture would deserve some explanation.

An adequate course that helped me understand more about NLP in tensorflow, in addition to the content provided in the deep learning specialization course

I'd like a bit more in depth (theoretical) discussion on embeddings, NLP, etc. I'm hoping the next Course (RNNs) gives more detail on the RNN, LSTM etc.

Explains all I needed to know to start with NLP in tensorflow but stays with basic knowledge. Further information sources are provided during the course

First two week was amazing! I did much work in Colab and started to memorize how to build a working model. On the third and fourth weeks, it was hard.

Good primer on NLP, picked up a good handle on using word piece tokenizers from TFDS datasets, wish they were applied to the Irish Poetry as well.

It was fun working with texts and understanding how TensorFlow enables us to access these powerful APIs with makes things more easier and faster.

I feel that there are some parts that are less detailed in defining a concept. other than that everything is pretty good and easy to understand

Good introductions to NLP, mostly for beginners. Exercises are not very good as they don't challenge you to get certain level of good results.

the content was really good but i hope if there were assignments in the end of every week it really help me memorize the code for long time

Very light on the brain. But all the models like an RNN, LSTM were covered. I wish there was more detail on the theoritical aspect as well.

The course was really nice and well explained. But I felt weekly programming assignments missing. That could add more value to the course.

I think you should include also in this course some graded progamming assigmnemts.

Those tasks realy help to increase the understanding.

Good course. Taught a lot about natural language processing. You should know how machine learning works in order to take this course.

It's complemented by Andrew Ng course on CNN. Not 5 stars because I felt that I had to jump to many concepts into the other course

This course is very basic with regards to Natural Processing I felt like but good at giving a structure to use TensorFlow

Detailed Explanation of the NLP and some problems related to it.But did expected more focus on the over-fitting problem.

Nice courses. Include embedding, RNN, LSTM, CNN ,GRU. It's better to have seq2seq, attention and more advanced model.

It would be nice to have the final programming exam as same as the first 2 courses, which is evaluated by the system.

This course teaches you how to prepare text, embedd, classify, and predict with lstm, gru or cnn. It is fun and easy.

I did NLP previously (poem generator), this task is like MNIST for NLP, maybe try to add something more interesting?

Good intro to NLP. It would be even better if instructor explains concepts like LSTM, Adam Optimiser, DropOut, etc.

It was an amazing course. However I would like to improve using other languages for example in spanish the corpus.