Amazing

Thanks

Great!

great!

Good!!

great

great

Great

good!

Great

good

good

best

Good

Good

nice

ggod

ok

The course material is good and I would give a 5-star for it. The reason why I took 1 star back is that the instructor seems to be not very well prepared for this course.

First, he used 'so' too frequently while lecturing. I am not saying that he should totally not use any filler words (like 'hmm' or 'um', and 'so' is one of them), but saying that using many fillers could cause distraction and confusion. As 'so' is one of the transition words, it implies a logical connection between 2 sentences. Using 'so' a lot was actually distracting me from following the course material because I had to identify which 'so' was a filler so that I could ignore it and which 'so' was a consequence indicator so that I could pay attention to the following sentence.

Second, he sometimes seemed to get lost with the slides. For example, from Week 3 Video "Learning Text Classifiers in Python" slide at 13:36, the slide was easy to understand by showing the codes saying "NLTK.classify has something called SklearnClassifier which could let you use some models from scikit-learn such as naive_bayes or svm and here are 2 examples", but his way of explaining the slide was quite confusing. This kind of "mistakes" cost me extra time to look at the scripts to make sure that I didn't misunderstand anything.

Overall I think this is a great course. I learned a lot from it. The assignments for the first three weeks were great in quality, and even though I had to spend some time on some 'unnecessary debugging ' due to their Autotrader every time I submitted my assignments, it actually was not that difficult to figure out. So I think it's still worth it.

I gave four stars because I feel the final weeks' content was way too general. The videos in the fourth week only gave an overview of the subject from a very high level, provided no coding examples with real-life data. I feel there was a big gap between what was taught in the lectures and what was required in the assignment of that week. Also, the wording of the last assignment was very unclear.

I would recommend this course to others because the first three weeks' content was great and you could learn a ton from the first three weeks' assignments especially.

I like week 1-3 of this course. week 4 is terrible though.

Week 1-3, Ilike this instruction and step by step assignment structure. I start to have some sense of NLP. However, week 4 is probably the week with shortest instruction. Very brief introduction to LDA etc, then a much much much more difficult assignment. It took me several days to read documentation and search stackflow to complete the assignment.

So, I finally know how to use regex in week1, start to know basic idea of tokenize and ps in week 2. and refreshed machine learning, actually, week3's ML instruction is better than course 3 of this specialization. Then week 4 is a hell. IF they really want to revise this course, I strongly suggest to have a clear case study to go through. This is a must for those who are not familiar with NLP.

A good course which introduces you to the basics of text processing and text mining in python and exposes you to tools such as regex, nltk and gensim. While the lectures and assignments do promote this learning, a lot of the criticism that is directed at the course is due to the auto-grader issues. You can easily side-step a lot of these problems by going through the forums. However, I do think that the course could have been better planned and executed, even IF the only purpose is applied text mining for e.g., better context and some exposure to theory or at least pointers to where more material could be found for self-study would have been helpful. However, I did learn some things from the class giving me a push towards learning more on the subject on my own.

wk1-wk3 are good. w4 is a little weak to build the connection between texting mining and coding. Moreover, it will be more straightforward if the lecturer teaches more about the procedure to deal with text mining. I just passed this course but don't master text mining technique through it.

It is still a good introduction to texting mining, a very beginning of it.

My suggestion is that wk4 should be reconstructed to make people really believe they can use what they learn in this course after they pass the assignments.

Finally, thanks the lecturers for introduction. Especially thanks all students who contribute a lot in forum. Without them, I cannot pass the assignments.

I think the course and content was interesting. I would have liked more material to look through tho. Maybe some more readings or somethings. I found specially the final week i was not feeling the help from the videos as there was so much actuall coding that was not shown or helped with in the videos. Its a tricky subject to translate the theory into the actuall code needed to finish the assignment. The final assignment took me closer to 15 houers rather than 3 as is indicated in the discription. Reading through the forum (as i spent a lot of time doing) i found that my experience seemed more normal than odd.

the field which is intruduced is quite exciting for me. before that course i knew regex, but after this course i gained confidence with it and learned it very detailly that i realised i knew regex superficially. With the assignments i think i got the necessary skillf for regex. However, it is hard to say the same thing for the nltk library. comparing the other courses of the series i expected a much more explanation. i had some difficulty as solving the problems, but with the support of discussion forum i got very enlightening hints. Overall i'm happy to get this course. thanks everybody!