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Learner Reviews & Feedback for Applied Text Mining in Python by University of Michigan

1,807 ratings
343 reviews

About the Course

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python....

Top reviews


May 04, 2019

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. :-)


Jun 26, 2018

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.

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1 - 25 of 339 Reviews for Applied Text Mining in Python

By David M

Nov 06, 2018

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.

By Li Q

Aug 10, 2018

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.

By lcy9086

Feb 22, 2019

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!

By Jian G

Mar 07, 2019

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.

By Alejandro C M

Feb 10, 2019

The instructor provided very low quality material.

By Michael T B

Jan 29, 2019

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.

By Aryan P

May 15, 2018

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.

By Niccolo A H

Oct 23, 2018

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.

By Eklavya s

Aug 05, 2018

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.

By Мирзабекян А В

Jul 19, 2018

The most discouraging course in specialization.

By David W

Apr 05, 2018

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

By David C

Aug 08, 2017

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.

By Dario M

Jul 19, 2019

The difficulty of the assignments is in no way related to the simpleness of the lectures.

By Zefang L

Jul 18, 2019

Great introductory course for text mining. Very help to learn nltk, gensim, etc. packages.

By Lin Y

Jul 16, 2019

This course is probably the worst amongst all other courses in this specialization. The term 'applied' in the course title makes you think that this course helps you to build practical experiences in text mining. However, not true at all.

By Yuhung C J

Jul 15, 2019

The last assignment is really challenging!!! Good course for text mining introduciton!


Jul 08, 2019

Contenuti troppo superficiali, gli assignment troppo specifici

By Linus

Jul 06, 2019

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.

By Fadhel A

Jul 04, 2019

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.

By naive666

Jun 29, 2019

Far from expectation, feel upset

By Shashidhar s

Jun 28, 2019

Ultimate course for any one to start with on Data Science using Python.

By shantanu k

Jun 24, 2019


By Hai Q P

Jun 22, 2019

Good for real researchers. I highly recommend.

By Samuel K

Jun 22, 2019

Good course with great content and lecturer however the assignments are all buggy and don't run in the Jupyter notebooks. This is frustrating to deal with in a paid course. Please fix!

By Sean D

Jun 20, 2019

This was a good course. Far superior to their machine learning course.