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.

The volume was too low for all the videos. I could hardly understand what sir was speaking. Very bad experience.

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.

One of the best courses on NLP with neural networks out there!! The instructor knows his game and knows that keeping things simple maximizes learning. The quizzes may appear easy and relaxed, however, the quizzes are to the point and are the key takeaways of the learning. The ungraded weekly exercises are the best takeaways of this short course. Hope in the future these weekly exercises could be used as assignments instead to differentiate between people who are really putting in the effort to write and learn the code and the people who are casually going through the course.

First of all, the course was amazing! I found it great for the following reasons:

- Laurence Moroney (Instructor) was very professional and clear while delivering the knowledge

- The introductions by Andrew NG were really nice

- Easy to understand codes and understanding of thr underlying principles

- Varied topics such as CNN, NLP & Time Series

- Very insightful by providing expert opinions about different ways of model optimization

I really enjoyed the course and I thank the instructor for the same :)

I already had some theoretical background from the Deep Learning Specialization from Andrew Ng, but with this course, I feel much more confident about building real-world applications with TensorFlow.

Great course with fun examples! Probably more valuable after completing Deep Learning Specialization/Sequence Models by Andrew Ng (https://www.coursera.org/learn/nlp-sequence-models)

This course covers the overview of NLP without going into much mathematical detail. In short time span, many things can be learnt from this course, helpful for the beginners.

Excellent course to take after completing Deep Learning Specialization

Great course as always

Excelente!

The course is really nice, especially if you just start working with TensorFlow.

But I think it could be better to have 1 week for all course, with 8-10 min videos, instead of splitting it into 4 Weeks with 1-2 min videos

More graded coding excercises would be useful, the way it was done in the first specialization course. The optional coding exercises have a lot of code that wasn't covered in clarity within the videos.

I think that it should have included some graded excersises, just like the ones in the second course. I really enjoyed this one, but I felt it could be a little more practical.

Concise explanations and nice demos leading into a very easily digested lessons. Covering every important fundamental aspects without being bloated by too much technicalities (which are only useful in a more advanced implementation). But again, basic is still basic. The quizzes def need more work as to not rely on a simplistic memorization problems (which almost doesn't exist on always-connected working environment) and instead should ask for actual concepts or understanding.

Def not deep enough if you pay for it, but a good one if you can finish it during the trial period.

The Course was good. The content was the introduction of Natural Language Processing and it does well in explaining the theory and all but I think they should have dived a bit deeper into the topic. Sometimes in between the course, it does feel that much exciting, because some part of the code showed does not have any proper explanation. Maybe I'm saying these things because I'm in sync with Andrew Sir's teaching earlier. But overall this course will be best for beginner and it should be a great start for moving into the NLP field.

The pros: An abundance of Python Notebooks that help to build intuition, fascinating datasets, and some interesting NLP applications.

The cons: This course feels like content in progress compared to every other course I've done so far. The material is too general and references other courses when it should use this opportunity to reinforce content from other courses, e.g.courses in the DL Specialization. The lack of required coding exercises, together with the reuse of quiz questions in Week 4, were a disappointment.

The video lectures are great! Well explained and engaging to follow.

But the exercises are poorly explained, therefore you often end up not even knowing what you want to achieve. The overall goal is clear, but since the input data is not known and not easy to visualize, having a working program feels like a pure matter of trial and error.

Also, some of the provided links to online resources are no longer active, therefore the "error 404" page is sometimes encountered.

As previous Courses of this Specialization, this Course is very suited to teach you the main concepts quickly. But it lacks the hands-on practice required to go into the implementation details. At the end, you won't have the minimum experience to usefully apply the learned concepts

The Course gives some nice code snippets one can reuse, but it just forms a starting point. In General the Course does go into any depth and I would recommend it only to beginners.

A little bit too simple/easy. Not as much practice required as the other courses in the specialisation.

The evaluation process is very simple and based on memory rather than on concepts. Overall, courses in this specialization do not motivate the student to learn. The Andrew Ng's courses are much more detailed in theory and evaluation. These courses stand nowhere them.

Echoing what others have said. The course information is largely links to official documentation and videos from other courses. The notebooks are cookie cutter follow alongs. There really is little content here.

This is a *very* surface level course. You might learn the mechanics of using tensorflow to perform some operations, but the moment something goes wrong you'll have no idea why.

This whole specialization was not even close to the quality I expected after taking Andrew's Deep Learning specialization. This one was especially bad because it didn't even have graded programming exercises, unlike the two first courses. By the end, I was just fast-forwarding the videos and solving the quizzes with almost no mental effort. Basically, if you have taken Andrew's courses, you don't need this one and you probably will be better off just doing free tutorials for Tensorflow and Keras.