Compared to Andrew's original M/L course, and the most recent Deeplearning specialization, this series of courses is very lightweight. The material is very good, well organized and clear. But it can be covered fairly quickly. This last one, natural language processing, can be completed in an afternoon, all four weeks. This is a bit annoying, as the courses appear so far apart, I have paid over $40 for each of these three, for what could essentially be a weekend course (all three courses combined).

Nothing wrong with the material, and I often use and refer to Laurence's code examples. I just wish there was more material in these courses.

Very elementary introduction to applications and scenarios in nlp. As has already been mentioned in other comments, the whole course can be compressed into no more than two hour long lecture and exercises over an afternoon.

The lectures consist of short videos introducing snippets of code and occasionally making claims but without actual notebooks with which people can play and reproduce results.

The quizzes through out this specialization have been written extremely poorly, testing irrelevant (if any) information about datasets and naming modules etc. The quizzes are so trivial that the fact the course grade and certificates are only based off performance on the quizzes makes the whole idea of paying to get certificates questionable.

The exercise notebooks are okay but are extremely redundant. After the great expectations built from taking Andrew's deeplearning specialization and machine learning course, I must say the first three courses of this specialization have been extremely disappointing.

I still want to thank the instructors and the team for taking the time and effort to build this specialization. Perhaps I'm just not the audience it was aimed at.

My recommendation to other learners is to first checkout the free tutorials on tensorflow website and keras blog, and then audit through videos in this specialization before deciding to pay for it (Also make sure to first check a few other resources, e.g. Deep Learning with Python textbook by Francois Chollet, the github repo for which is public and the notebooks are almost exactly the same as here but more in-depth).

NLP basics. Missing a lot of things. This entire course could have been made in to a single weeks 5 mins video.

One star for the ML poetry and one star for the content. The content can be learned in a few hours. Not much more than a simplistic tutorial on some simple problems. Dilutes the value of Coursera specializations.

Not challenging , very much beginner level course , shouldnt be tagged as intermediate in my opinion

The course if worse than even an overview course. It just shows you some random code and you have tyo try assignments yourself without any knowledge of nlp. This is not expected from deeplearning.ai.

The course is extremely basic and all the materials it covers can easily be covered in just one article. Doesn't build any transferrable skill.

Very lightweight course - not more than an hour of real content. Extremely disappointed by this.

This course has very little materials. One can simply do it in an afternoon. Each video is around 1min. Explanations are very poor. Tutorials on Tensorflow websites are much better than this.

I feel like I could have learned more by reading on stack-overflow - I didn't learn much here. Quality training materials could have been better.

Too basic. It could have been done in a simple blog post

A quick and practical overview of NLP with Tensoflow keras module.

Lightweight course. Probably about an hour of real content.

Amazing course by Laurence Moroney. But only after finishing Sequence Models by Andrew NG, I was able to understand the concepts taught here.

Excellent course to get you started in NLP.

I was waiting for a course that covers NLP, this course covers all topics of NLP with added value working with Tensorflowto facilitate implementing projects, and it's well designed, and Dr. Laurence is amazing, his explanations are useful and easy to understand, Thank You!

A very good introduction to NLP using Tensorflow. This is definitely the best of the Tensorflow series so far. Excellent pace and interesting short projects. I would highly recommend this course to any beginner on the subject.

I was able to very quickly get a grasp of how to approach text data and gained both an understanding of how to represent language-based data as well as how to apply deep learning to do some pretty amazing things. Great course!

Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!

Great course for anyone interested in NLP! This course focuses on practical learning instead of overburdening students with theory. Would recommend this to every NLP beginner/enthusiast out there!!

Excellent course. Teaches NLP thoroughly, going from the basics such as tokenization and padding to complex topics such as word embeddings and sequence models (like RNNs, LSTMs and GRUs).

I am enrolled and paying 49$ a month for the 4th course in this specialization and it hasn't even been released yet. Not sure how it is fair for them to release a specialization that isn't complete and take people's money while they finish the last course... Other than that... So far, this has been a good series of courses in the Tensor Flow in Practice specialization. Although it feels like a slightly watered-down version of Andrew Ng's 5 course deep learning specialization.

Missing the colab files

There is not much content in this course -- everything in this course can be covered in a week. It's not worth spending your $49 on it. Moreover, there is no graded assignment.

Like the other courses in this specialization NLP in Tensorflow is an outstanding course. The content is high quality and really useful to help you build an NLP model from scratch.

The lecturer guides you through the process adding a little piece each lesson, showing you the results and giving you the chance to try them yourself on a lot of different notebooks.

While there are no graded assignments, you are still given the chance to build a model by yourself every week and put into practice everything you learned.

This is a really hands-on series of courses and as such you have to seek theory explanation somewhere else. Despite that, to help the students who didn't take the Deep Learning Specialization there are numerous resources linked in case you wanted to develop a better understanding of the subject.

I think that the best thing is that it's not a Tensorflow tutorial (you can find that online), but it helps the student develop a way of tackling NLP problems, explaining the building blocks necessary to create a model.