There is room for improvment in this module.

Please find below, some comments that may help improving the content:

1. I found that there is a lack of hindsight on the place of these methods in NLP processing chains and how they are combined. 2. Errors are present in the slides and should be corrected in the content as well (especially in week 2 Vclass vs V + incorrect addition of frequencies which made several slides inacurate). 3. Using the term "dot products" limits the understanding of the transformations performed in vector space. In my opinion, it would be worthly to speak/ write "projection" instead on the slides.

4. Make a clear plan within a week that recall the place of each concept into NLP processing. It will give a better insight of the importance and the place of each concept by looking to the utlimate goal of producing an NLP processing chain. 5. Make a better disctinction between sentences (tweet) and document.

6. the content is, in my opinion, too fragmented

The notebooks are regular, some cells marked as "hint" and those contains info about functions required to pass tests when I submit the notebook. In the other hand, some functions are equivalent, for example np.dot is equivalent to dot function for a numpy array instance. However, some tests evaluate if cells contains np.dot even when the output is correct. This is so confusing and I spend a lot of time checking my cells when even the output is correct. Concluding, the notebooks can be better, instead of say "print the variable x to know about the structure" you can use visual aids to show what we are doing, I'm lit tired of checking my variables, check the structure and check and check every cell. Another advice, please stop of using list, set and np arrays together without any mean, I mean just use dictionaries and np arrays and this is all! I'll qualify this as 3 stars because the notebooks. I learned about and thanks teachers.

The video explanations are too mathematical, and maybe a bit short, as they are complex information that could be explained further.

I think the final test after the week's videos was a good idea, and I missed it here.

The "intermediate" code "assignments" should also ask you to code stuff, not just read what other person has coded, in my opinion.

Finally, I think the final programming assignments are not very well explained (especially compared to those of the Deep Learning Specialization), I was very lost most of the time and didn't really know what I was doing beyond applying some math. Actually, for the last assignment, which was supposed to be a translation from English to French, I didn't see it at any point at all. I basically don't know what I coded there.

The course is clear, understandable for people have basic ML background. It's very practical too. Never get bored.

I'd give 3 star as I think the grading system for assignment should be better. For example, when it's grading KNN, it requires the output cluster element is following an exact order, so you won't get the point if the output cluster(as a list) in a different order, even though it outputs the same cluster of element, which makes little sense. Funny thing is the grading standard for KNN requires the order of cluster element following distance from largest to smallest, not the other way around. Meanwhile, I did suspect some of the assignment answers were incorrect. However, no one has replied for my post in the forum.

It looks like first scetch of the course. All programming assignments has "try your own examples, check what you just implemented". Week 4 translation - after performing gradient decent I didn't have oportunity to test translation. Or the buggy example with Country Capital matching "if you use euclidian distance Ankara is capital of Japen". After Deep Learning specialization where Andrew Ng so passionately explain you everything, guys that are reading from a teleprompter looks boring and unprofessional. Like "if you can't explain it in YOUR OWN words you don't understand it." But Labs were useful and hints sometime too. That is good. I guess most of the topics were covered, thanks for that.

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.

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.