I had a problem with the grading for assignment 4 in course 1 and after quite a bit of troubleshooting and posting a forum question I found a similar problem that had a suggestion that helped me fix it. I would have liked to be able to get some feedback from an instructor directly to save 2-3 days of struggling to resolve the grading glitch myself.

With respect to a first course in NLP this it is ok but as others have said most of the code is already written. If you are wanting to understand the intuition of NLP this can be good but then difficult to apply yourself in practice.

if you compare this course to that taught by Andrew directly, this course is somewhat lacking. I love how Andrew teaches his students (like he did in ML course and DL Specialization) using direct writing on the screen and using natural speaking rather than speaking like a robot (yeah you just read texts when explaining something).

Many mathematical concepts but lacking explanations.

You placed too many coding assistants in the programming assignments, making doing the assignments is just like a fill-in-the-blank question. not challenging.

Some videos could be better presented. For instance, start explaining that k-NN will not be implemented the usual way, but using a fast approach.

Also, the validation of the submissions could do better. np.array(list) works just like np.stackv(list). del(foo[bar]) works just like foo.remove(bar) and matrix.squeeze() works just like np.squeeze(matrix).

I know it is difficult to get all the possible code combinations. But probably the code could be tested more as input/output than grepping the code for keywords.

the material is super basic and from scratch, like a stone aged one, many many ways we could have talked about KNN and other ML methods using sklearn, we wasted so much time implementing those, and also please work on your comunication skills as i was not comfortable with the instructors being so uncomfort in front of the camera, not a huge deal but it's just a tip, hopefully the rest of the specialization is better, looking forward to them.

Course is good, not great - mainly let down by a few presentation issues, mistakes in slides and lab code, and a rather picky grader. This sounds worse then it is - there's only a few of each issue and in some ways it's a good test to see if you're paying attention :) Overall decent course to brush up on some NLP foundations, but you better have a good background in Linear Algebra if you don't want to take a large tangent.

I must admit that I am quite disappointed with this course. The explained material was interesting, but there were many errors in the assignments. It happens that people get 0 points for some exercises because of internal problems with the tests. In such cases, it is not even known what the problem is exactly. Hence you must do exercises exactly as the professors wanted, even if there are other approaches.

Course touches important concepts, but only on the surface, not really explaning things in-depth.

3min videos explaining PCA or LSH seem just too short.

Also, big minus for no support/forum for the course - one assignment contained typo and unless fixed, you'd always get negative grade. But there was nobody to talk to...

I'm sure Deeplearning.ai can do better.

This course has plenty of wonderful material and very deep content, however the assignments and quizzes seemed to be fairly trivial in testing knowledge. I appreciate the material and will keep it as references for NLP projects in the months ahead, but I do not think that passing the coursework is indicative of learning the concepts in any meaningful way.

Not bad for beginners. Assignments are quite easy as they are almost done for you, however usefull to look carefilly at them to understand the idea behind.

I rated 3 stars as this course is not really about NLP, but more about other stuff. For example week 4 hashing tables exercise is interesting but not NLP related, more about efficient KNN finding

I found too many issues along with a lack of clarity in the programming assignments. Definitely needs to be refined.

The lectures are decent and the reading sections are nice, which displays the previous presentation contents.

The additional labs are just OK, not great. Some contain a lot of code that was not exactly clear so much of it was skipped.

Assignments were way too easy like write +1 in this section where there is None. I think 2nd course has improved upon this giving you first way to do it yourself and then giving you general tips and then additional hints. If Week 2 of Course 2 has the same structure I would be glad, but the assignments in this course were way too easy.

I think it is an okay course for some basics. Most tasks are decent and well explained. Unfortunately there are quite a few inaccuracies that only sometimes get corrected and can lead to spending way more time on tasks than necessary, translating incorrect information on the slides into the correct ones or just simply be distracting.

Not sure of what I should master after this course. If it's the theory, or, say, the main principles, then OK : I have an idea of how NLP with vectors spaces works. But I feel totally unable to implement it, concretely. Perhaps it's not a problem if, in fact, we are just shown this technique as an initiation, before more complex ones.

Overall, the course covers a good content and informative but it lacks of in-depth discussion of each topics of NLP. It's not as thorough as Deep Learning Course. Also, the programming assignment is too picky about which functions of Python to be used while there are several ways to achieve the same outputs.

You guys need to work on the programming assignments, especially the teaching is below par as u guys didnt differentiate the word embedding we found by word by doc model which has to be a whole number and the embedding matrix which we generate from -1 to 1 which captures relationship between words.

I would like to have seen more breadth and depth in the course, and of course I have my perpetual beef with certain Coursera courses like this one that grade the programming assignments by looking for code features (which must be matched exactly) rather than evaluating results.

not enogh explanation of topics ... please give in detail explanation of topic . It seems like after this course i need to do few more courses on the same topic cause it just introduces to the concepts and not giving in depth knowledge like other courses of andrew ng.

Great information but not enough opportunities to practice skills or internalize concepts. The assignments are too easy and don't let you flex much brain power. I feel like the lack of any repetition will result in almost immediately forgetting material.

I feel like feed back and testing of your code code be more detailed to help pin point coding mistakes. I was spinning my wheels at the end and did see any solutions or discussions on my issues. I still passed but would like to see what I did wrong.

The lecture videos are a little short but provide some fundamental insights. It would be better if the videos were longer and more detailed or some supplemental resources. Overall a good course if you are a beginner or don't know where to start.

Shallow explanation in some topics in the lectures. It would be great if lecturer explained topics in more detail, and answer questions like why we use this model, show how it was created, pros and cons, and show why it works using math proofs.

I like the way the course use simple machine learning technic to solve a complicated problem,

for someone who likes mathematic a lot could be done in explaining mathematic concepts,

the assignment could be improved by using unit testing.

Andrew, come back with us!

Although very interesting, the course spend too many time and many student efforts in details like PCA and LSH. This is a good way to loss the big picture during the course.

Compared to prior deepLearning Ai courses. the lecturers were very robotic and un natural. The explanations were much less clear and less effort was made to explain the intuitons behind formulas.

Lectures were very straightforward and digestible, however the assignments had inconsistencies within themselves, especially between the written instructions and the comments in the code cells.