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.

Lightweight course. Probably about an hour of real content.

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.

Not as strong as the previous 2 courses in the specialization. Each step building on itself (embedding --> LSTM --> stacked LSTMs) did not really show much improvement in the actual exercise. Also the main pieces that impacted accuracy and loss the (buffer size and batch size using SHUFFLE and BATCH_SIZE) were completely ignored. I also wish there were graded exercises that forced me to learn both the data ingestion, tokenization, and training pieces. I struggled through those in course 1 & 2 but it paid off in my understanding. Data preparation wasn't covered as in depth as the computer vision course either (i.e. how do I deal with large data? can I flow them in like the image generator?). Lots of broken links as well (some people might not know to go find the github repo to get the optional exercises).

Quality of this course is not good compared to previous two courses. It is super percised and many things are not well-explained or explained very briefly! The second course was better structured and valueable. I was hoping that this one is as well, because I am very interested in NLP than Image Processing.

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!!