great course

Veri good

great

good:

* some of the content is well-explained

* provides good solid knowledge about the background and implementation of common NLP tasks

less good:

* notebooks (and content generally) are unevenly distributed

* significantly stronger focus on ML, rather than on the NL side (this is consistent throughout the specialization)

* some of the explanations (e.g. in week 2) aren't clear

* specialization could be structured better -- word embeddings are introduced in course 1, but the in-depth discussion is here in week 4; would perhaps have made more sense to have that content build on itself

In this course, you will learn to build an autocorrect model and different methods of building this model. The course felt a bit rushed with a lack of detailed explanation, students who are familiar with the concepts of NLP from before starting this specialization won't face any problem, but students who had just begun learning NLP through this specialization might feel a little difficult.

The course material is good and you can learn new things, you can exercise python skills a lot as the assignments are quite long. However, the tutors are not the best in explaining the material as well as the videos are a bit vague. It would have helped if the tutors were a bit more experienced in teaching, but still overall good!

well I did deep learning specialization earlier things are mathematical, but here they don't go much into maths, and please make some concept chart, to link different algorithms.

This course is good, but too fast for a beginner. By the way, the explain for the problem doesn't have any orignal idea for it.

BIg bug in week4's assignment! I don't know why not fix it. It turns out that I just train a dummy network

The instructors look like reading scripts and indeed a bit awkward.

Topics were not clearly taught by instructure

This was an okay okay course

Doubt support can be better

Not much useful

Generally good content, but there are several issues. The quizzes for each unit do not always reflect the material for the unit; they are obviously from other units within the course. Many of us have pointed this out on the course forums and reported the incorrect content, but it remains. There are also *lots* of typos and incorrect comments/text/captions in the videos. Some of them have pop-ups that point out the incorrect info, but many do not. The notation is inconsistent between slides in the course and differs even more between the slides and the assignments. It feels very sloppy. I have reported several of these, but no action has been taken. The creators seem to have created the course and walked away leaving a ton of errors and inconsistencies. There does not seem to be ongoing support for the course, even when there are clear, egregious errors.

Too simple. The practical assignment is more not about learning embeddings, but about running about forward and backward pass on the shallow network.

I think that model evaluation and test could be covered better

It's bullshit. Grading on assignment is faulty. No support. No one to help on it. Reached out on community as well. No response from deep-learning team.

Most of the algorithm and logic was implemented beforehand, I did not get to implement much, did not feel good after completing the 2 courses

Just a stupid course