Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)
The video lectures are good, but there are many issues with the Jupyter notebook assignments.
By Alexandros B•
poor organization of the lesson and many many mistakes during assignments
By Alex M•
Instructors did a poor job of preparing students for the assignments.
By Ji S•
Too coarse, quality worse than other courses in this specialization.
By Abhishek J•
it was so basic ! i was expecting some more detailed course on nlp
By Laure C•
I found the course quite dry and hated the auto grader...
Far from expectation, feel upset
By Elliot B•
I found this course quite confusing and often unrelated between video lectures and assignments. The lectures maybe covered an assignment in broad strokes but to actually answer any of the questions needed extension research from the student. I felt like I was teaching myself the base content. At that point, what is the point of the lecture videos if they provide no value. I almost stopped my subscription and gave up on the data analysis specialization based on the quality of this specific course. Previous courses in the specialisation did provide useful information in lectures which was then extended upon in the assignments. This method of teaching something in the lectures then building on finessed usage in the assignments is a much better approached.
By Gerrit v W•
Compared to the first three courses in the specialization (which were all excellent) this course is total shite. The instructor just glosses over concepts at a very high level and clearly put little thought or effort into planning them. The only thing about the course that was remotely positive were the assignments. They were all poorly worded but by completing them I did learn several interesting/useful applications of Gensim and NLTK. Unless your goal is completing the specialization I wouldn't waste my time with the course. It's by far the worst Coursera course I've taken. Shame on the University of Michigan for putting their name on this course. It reflects poorly on them. I recommend they either remove it from the specialization or redo it.
By Saeed V•
This course is a real waste of time! They try to teach you how to swim without water involvement!!
I like the lecturer and his still in the first and second weeks. But, starting from 3rd week, the lecturer teaches nothing. He explains some basic concepts and you should answer the detailed/technical coding assignments. The assignments have nothing to deal with the lectures. The lectures have zero to very limited coding explanation, even though the course name has "APPLIED" and "PYTHON". I learned nothing from the lectures but I passed both 3rd and 4th assignments with 100, thanks to StackOverflow and online resources. Plus, outdated auto grader and material!
I am wondering who gives this course 5 stars. Fake reviews?
By Christopher I•
The lectures for this course are terribly uninspired, giving very little useful information--the vast majority of it is the professor talking about obvious aspects of language at a very high and useless level. The autograder is frequently breaking for very minor things (such as returning numpy.float instead of float), the questions on the assignments are often misleading, poorly worded, vague, or just generally not very helpful. All in all, this was one of the worst MOOCs I have ever taken, though the Coursera bar is pretty low. It does make me wonder why I bother to pay at all--oh right, Coursera now makes not paying a major inconvenience to course progression.
By christopher h•
Compared to other courses in the Applied Machine Learning focus, this is so far the worst. The content and quality are poor. The lecturer is too slow and fails to prepare the student for the assignments. First week is very basic and ends with an assignment in regex. There's plenty of regex resources out there. 2nd week moves forward but finalizes in an assignment that involves concepts not covered in the lecture (ngrams). Weeks 3 and 4 contain too many errors in the lecture and autograder (use of AUC, finding minimum of a sparse array). UofM should rebuild this course.
By Guo X W•
This is my least favourite course in the specialisation. Natural language processing is an exciting field and I think there is a lot more potential to enthuse and engage students. The instructor scratches the surface of text mining by going through brief sets of codes on ppt slides. I thought it would be meaningful to use more real-world datasets (as in the previous courses in the specialisation) and have students follow through some examples on Jupyter Notebook. I also felt that the exposition by the instructor was not the most intuitive or lucid. It could be much clearer.
By Matt P•
This course was much less helpful than others in the Specialization. The assignments are poorly conceived, and submissions are beset by finicky autograder issues. Certainly, data cleaning and code debugging are critical skills for text mining, but I find it difficult to believe that "try to understand what output a function should submit so as to satisfy the current autograder" is a useful way to teach text mining.
I hope this course will be re-done to bring it in line with the quality of the others in the Specialization.
By Denys P•
The course is a joke. Its outdated and not supported, you literally need to spend hours to try and figure and emulate versions used by autograder and even the file structure for files used by default is not accurate and you get file read errors on predefined by them functions on their own virtual environment and need to fix these for them!!! The virtual machine env provided is super slow so need to use your own. Very bad user experience and horrible use of time!
By Dr. D W•
What a horrible course. Especially the assignments are such an unbelievable waste of time. Instead of focusing on important concepts and applications, one has to spend hours one "pleasing the autograder" by renaming columns and reading the discussion pages for the correct interpretation of all the ambiguously formulated questions. Very sad! Would be good for everyone if this was removed from the (otherwise great) series "Applied Data Science in Python".
By Maximilian W•
Serious sub-par course in the specialisation. The lecturer is good, but sadly the assignments are terrible. Thus the reinforcement - and reliability to problem solving - of the content is poor.
Given the high standard of the first three modules in this specialisation, this is really a shame. I would urge learners to consider whether there is much point in doing this course (other than to get the specialisation completed).
By Eduardo C F•
I was under the impression that the course is incomplete, especially week 4, which has no notebook examples of the theory presented. I needed to look at other sites for basic information. I could only complete the exercises because they are easy, otherwise, with the code presented during the course, I would not have been able to. I suggest strengthening the example code in python (see week 3, good code)
By Ruben G C•
I have to say that the previous three courses were very well explained, with good examples and python code. However, this course is not well explained nor documented. It is a pity that the quality of the whole specialization program gets considerably reduced due to this course. The assignments do not allow you to learn and you may not pass them due to small differences in the coding.
By Justin M•
Videos are so high-level that they don't help at all understanding the necessary code. Assignments have spelling errors and ambiguity. Week 4 is missing the sample code notebook. I eventually found the sample code notebook in the forums, but this was a big cause of frustrations as I had zero context for how to do the assignment.
By Nicholas P•
Unless the instructional staff updates the programming assignments to reflect updates in packages and ensures they can run without additions, do not take this course. It is a terrible reflection on the University of Michigan.
By Didier C•
The subject is interesting however the lectures are too shallow and the assignments too difficult. You should be expected to do more study after the lecture for sure but for this course, it was a lot.
By Venkata S M B•
Not a great course. I'd skip it. The assignments were just trying out different parameters. Nothing related to machine learning/using Python was discussed in the class (may be 2%). Didn't lean much.
By SHAHAPURKAR S M•
Video lectures are just been run through. No clear explanation at all. On the top of that, assignments are freaking difficult being totally irrelevant to the material taught in the video lectures.