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.

It's a very high level overview, I was expecting a bit more detail. The programming exercises are very basic, it felt like there could have been less but more advanced challenges to solve.

Interesting, but surprisingly many quality issues. Some topics, such as naive Bayes classification, need a better motivation (explain intuition and connection with Bayes theorem first).

The topics were interesting overall and the lectures even though rather short were still rather informative. Too much focus on specificities of libraries and frameworks in the exams.

While this was a great introductory course to some of the basic tenets in NLP, various ancedotal examples were too convoluted to be useful in gaining an intuitive understanding

This course provides you with a good but basic start to the world of NLP. Week 4 LSH and Hashing should be explained more clearly. Assignments are not challenging.

Basically lecturers' delivery is not so good that you could get distracted easily.

Often, a video contents and a jupyter notebook don't match to each other.

Seemed easy to me. Rest all is good, the explanation and assignments.

I am reducing star by one rating because of the interface for assignment is poor.

Good coding part. For my background the lecture material was a bit too easy. However, if your intention was to keep it easy, then good job!

The course misses depth and needs to focus on applications of these algorithms rather than introducing more and more algorithms so fast.

The videos were too short to properly explain things and the notes sections after each were basically just screenshots of the video.