It is no exaggeration to say it took me longer to complete this course than the first 3 courses in the specialty and the time was utterly wasted. I wouldn't object if I felt like I was learning new skills but it is mostly battling a poorly constructed course, with terrible assignments, a broken autograder and a Professor who is utterly disinterested in the education of his students.

When considering this course we need to separate the subject (which is fascinating) and the tools (which seem quite powerful) from the course itself. I had really high hopes during the lectures in week 1, where the videos are stronger and close to a well taught university lecture than others in the specialisation. However the assignments and the autograder issues are too great to ignore! Assignments are poorly worded (in one question it is literally trial and error) and the autograder often breaks. There are cases of people spending 10+ hours on work getting incredibly frustrated by the lack of feedback to find out the solutions were correct and the autograder was playing up.

Very painful going through this course although i have quite well coped with course 1-3.

But this course seems lack of systematic structure of building the knowledge, it just walked through the topics quickly and extensively. I had to spend a few hours to learn about the whole structure of text mining to build in-depth knowledge, more than 20 hours to watch the online nltk & genism tutorials cause i m new to text mining & nlp.

just hope the course can simplify the complicated topics such as where we are in the whole process, what's it, why we need it, working theory, coding, how we use these parameters, etc. to make life easier.

I would see autograder and unspecific instructions ruin this course.....Sometimes you know how to get the answer and the answer looks just right! but you still cannot get passed! I would not be taking this course if it was not part of this Specialization........ Improvements need to be made!

Instructor does not explain concepts, just superficially goes through subjects.

Some lectures lack coherence between subjects. you wouldn't know what is the relation between topics.

But it introduces some basic stuff which worth knowing anyway.

This is almost a waste of my time. The structure can be clearer and the connection to Python is outdated. The assignments are poorly designed. The instruction is not effective.

Curriculum is valuable but the course quality isn't on par with the other Applied Data Science using Python courses by University of Michigan. Week 4 assignment doesn't do enough to bring all the previous topics together in a realistic application. Week 3 lectures and notebook have teach the use of a scoring function wrongly - an issue addressed in forum threads for months but no edits to the video lectures and notebook have been made as of yet.

Instructor was poor. Inadequate coverage of the material in the lectures, some questions not clear as to what was expected. You can do better reading a book on this subject on your own.

The instructor provided very low quality material.

This course makes you give up on data science and MOOCs.

Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on.

I highly recommend stay away from this pathetic specialization.

This course give the basic idea in each module existed in text and natural language processing kits. A lot more for self-explore, but this will intrigue to begin sooner and learn wider.

I really wanted to like this course, and there were some redeeming features, but overall I'm unable to recommend it in its current state. IMO, the lectures were at much too high a level while the programming assignments were very detailed with vague instructions and little guidance. There was no link between what was discussed in class and how the fine details of the assignments were to be understood. In addition, the course was published with errors in the auto-grader and no resources in the Resources link (not even slide decks from the lectures, so to review material you were forced to re-visit all the recorded lectures which was very inefficient). My recommendation to Coursera and the Univ of Michigan is to completely re-do the course, doubling the number of lectures to provide not just the broad overview of the topics, but also some detailed descriptions of recommended ways to implement what was discussed. I would also recommend using Professor Andrew Ngs Machine Learning course as a guide for how to create great programming assignments, with detailed PDFs (typically 5-6 pages) describing what is to be done AND WHY (linking back to the lectures) and "telling a story" that is cohesive and leads the student to create something end-to-end (in small steps) that does something amazing by the end. The programming assignments in this course seemed, in contrast, to be a shotgun blast of "do this", "create this", "make this happen" with little context of how the small pieces fit together or what the overall goal of the assignment is to accomplish -- and at the end, a feeling of "I passed the autograder's expections, but have no idea what I've really done or why". There were so many great things that could have been done with the Text Mining topic, and this course touched on just a few in a very haphazard way that simply left me confused and wondering why I spent so much time to learn so little.

The most discouraging course in specialization.

I am an experienced online course learner, both with MOOC's and online courses through accredited universities. Unfortunately, in it's current form, this has been one of the worst classes I have ever taken. While it does have some interesting content, the delivery is sometimes wandering and more of a high level overview than a concrete, here's-how-you-do-it, practical class. The assignments also suffer from ambiguity and sometimes outright forgetting of explicit instructions. Moreover, workbook-type examples are often lacking. Although I'm very disappointed in the execution of this class, there is potential if these problems are addressed.

As an aside, after completing this class, I find it hard to believe that almost half the reviewers gave this class five stars. There are some fundamental problems here, and I almost gave up completing the rest of the series because of this one course.

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Instructions in programming assignments are misleading or poorly worded. This is an issue with every module of this specialization but Text Mining has been spectacularily bad. You need to spend hours browsing the discussion group just to figure out what is expected. Mentors are doing a great job explaining in the forum, but there is no feedback loop - the instructions are never corrected. Sometimes you see a forum post about a misleading or simply wrong instruction, that is dated 6 months ago, and the instruction still hasn't been corrected. It's like no-one cares. I feel like 70% of the time I spent on this course wasn't learning Text Mining, it was dealing with ambiguous instructions or autograder issues.

Unclear assignment instructions, buggy autograder, and no instructor help.

I am a Data Engineer with a degree in Computer Science who wanted to learn more about Natural Language Processing for a small project I wanted to build. I had no prior knowledge of NLP other than some regular expression work from college and a basic knowledge of what tokenizing, tagging and classification were at a high level. This course was a great introduction into the field and has given me a solid applicable foundation to continue my education. I wanted something that was light in theory and heavier in application and this course hit a great balance. Contrary to many of the other reviews, I didn't have a problem with the autograder, most of the time I got an answer incorrect was due to not reading the question carefully enough. The assignments were great in my opinion and actually helped drive home the points made in the lectures. I recommend this class to anyone who wants to get their feet wet in the subject.

This is a solid intro course to NLP that covers the basics. For what it is I do think it deserves a higher rating than the 4.0 it currently has. I was worried about the amount of complaints regarding the grading machine when I started, but I was fortunate to have only experienced a very minor, inconsequential problem. Maybe I was just too traumatized by grading problems with other courses (*cough yandex big data engineering cough*) that the grading machine in this course in comparison is pretty reasonable.

For further learning, I discovered the NLP course in the Advanced Machine Learning specialization. I must say that is much more in depth and cutting-edge. Would totally recommend it as a sequel to this course.

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. :-)

Would love to see these courses have more practice questions in each weeks lesson. Would be helpful for repetition sake, and learning vs only doing each question once in the assignments.

Autograder is a disadvantage that sometimes can take many hours to figure out. Also, this course was a let down compared to the previous in the specialization. I wish there were more examples.

I have taken and passed all the first four courses in this specialization, and very much liked the first three courses. But the quality of this course on text mining is far below the average level of the first three. Go find some other courses if you want to learn text mining with Python.

There are too many areas of flaws in this course. I am only highlighting the top 5 below:

1. lacks good connection throughout the course content. This problem exists almost everywhere, both from slide to slide within a video and from video to video. Many times you would have questions in your head like “why is he talking about this?” or “what is this?”

2. use example just for the purpose of showing examples. Don’t really explain the point it is supposed to explain. In many times the examples do not provide clarity, but raise more confusion instead.

3. assignment tasks either too simple, or remotely related to what is introduced in the course. The worst case is assignment in week 4, where the assignment is so poorly constructed. You have to spent days to figure out the right answer. They call it “debug”, but there is nothing wrong with my code. I would say it is more of a process to “try to figure out what the instructor is asking for”.

4. talks too much about the theoretical things, not very good introduction of using python. Even when python code is demonstrated, it is almost always in a very abstract way. This is significantly different from the first three courses, and very annoying. You would need to spend about the same amount of time googling how the packages work as I have never took the course.

5. Repetition of content already introduced in previous courses, i.e., machine learning basics.

I hope that the author of this course is fired. I had hoped for more review and exercises on topics such as sentiment and topic analysis, things that are PRACTICAL. Instead the focus was on things like document path similarity, part-of-speech tagging, and counting words in spam vs. ham documents. Adding to the frustration is the autograder, and the arcane formats for submitting answers. Wish that I could give this less than 1 star, it definitely earned it.

A complete waste of time. You are better off Googling the concepts as the explanations are absolutely inadequate. The homework is nice and challenging but the material covered in the lectures does not prepare you to complete it. You are pretty much on your own. Too bad that you need to take this course to complete the specialization. Definitely not worth the $80. Very disappointed!!!!

One of the worst courses I have ever attended. The subject is treated on the surface.

The exercises are sometimes not covered in the lectures. The auto-grader is badly configured.

It was annoying and frustrating to do the exercises. Sometimes an untold oderering of the results was expected. Sometimes an untold normalization has to be done.

The professor is wooden. The quizzes are ridiculously easy. The programming assignments nearly impossible. Beware the hidden workings of the auto-grader. If you're very lucky, one of the other students will prompt the TAs to action in the forums. This is, by far, the worst course in this specialization.