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

2,131 ratings
403 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


Aug 27, 2017

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!


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. :-)

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326 - 350 of 396 Reviews for Applied Text Mining in Python

By David B

Aug 15, 2019

This course teaches basic, practical skills for text mining with Python's regular expression (re, pandas) and NLTK package. While the lectures do not go into much depth and are typically too slow or too fast, the assignments are good exercises for learning basic text mining techniques.

By Vivek G

Sep 19, 2019

Only useful for coarse understanding of the topic.

By Alexander W

Sep 03, 2019

This course is interesting and about a very important topic, but it urgently needs an update!

By Carlos F P

Oct 03, 2019

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.

By Amit B H

Oct 16, 2019

The course wasn't totally bad but it definitely wasn't as good as the first three. I felt I was thrown in with insufficient tools to cope with the assignments. Relying on the internet is important but in these cases, you have to rely on it quite heavily. On assignments 1 and 3 in particular, Upon final submitting, I felt I didn't learn much at all.

Specifically with regexs, I feel extremely insecure with my regex skills and that is an understatement. I don't think that is something that should happen after a text mining course.

The following remark *isn't* a crucial one: For a non-native English speaker understanding the language could sometimes pose an obstacle. Now, decoding the lecturer's accent is yet another obstacle on top of the former. Lecturer with an American accent will obviously be the best choice.

By Mike W

Nov 12, 2019

Compared to other courses, there's a disconnect between what's covered in the lecture and what's needed to complete the assignments; the lectures at times have a more theoretical flavor. For a course with "applied" in the name, that's a more significant mistake.

By Alex W

Nov 15, 2019

Really poor instructions on week 4. Overall, was a great course that was a good intro to the text machine learning tools in Python.

By Anand N A

Nov 17, 2019

This course contents was good, but the assessment was really bad. You guys need to fix the autograder issues ASAP and I feel the instructor was not taking as much care as others to set the autograder propertly. Lot of time was unnecessarily wasted I would say as the instructions could have been better. Very disappointed with the assessment. Course is very useful and valuable.

By Wenlei Y

Nov 20, 2019

This course compared with the others in this specialization, is not-as-well organized. You might have to spend lots of time working on the assignments by yourself (i.e. you cannot find related guidance in the course materials); There is less helpful online information, compared to course 1-3 in this specialization, either - so it is a little painful to do these assignments. However, the tools and the theories behind them are useful and powerful. If you are really interested in text mining, you will benefit a lot! The instructor is passionate and humorous.

By Farzad E

Mar 18, 2019

It gives you a better understanding of SVM and LDA after taking the third course but they have failed to provide enough examples and exercises. Not every module has a notebook unfortunately!

By Peter B

Jul 11, 2018

I have major qualms with this course. So far in the specialization, this course is certainly the worst. *The autograder is terrible, having had serious, known issues for 8+ months at the time of this review.*The course content is incorrect, teaching learners the incorrect way to calculate roc_auc_score. *The course blows through certain topics, like Part-of-Speech tagging & Parsing sentence structure, leaving learners like myself without a good overview. I don't even have a good set of links to learn more. I can run a few commands and understand why it might be important, but I have no idea how to use it in practice. *Unlike other courses in the specialization, this one doesn't have good links to interesting academic papers or real world applications.*Unlike other courses, every week does NOT include a weekly Juptyer notebook.Here's a simple solution - give Uwe, an excellent and active Mentor, the permissions to fix this broken course. On the plus side: the instructor is ok, the topic is interesting, and this course really only feels terrible relative to the excellent courses in this specialization. I can still hardily recommend the specialization...

By Pascal R

Oct 08, 2017

First Coursera course I've taken with mistakes in the material and the grader. Also the first course where they mostly decided not to provide notebooks to review the material but instead made you scrub through the videos to find the actual code. Lastly the assignments were not terribly well tuned to the lectures (which were decent) and didn't make me feel like I had a great grasp of the material.

By Saravanan C

Aug 12, 2017

Liked the simplified content. But minimalist approach coverage of concepts - could be better. Tactical/Operational support, responsiveness from the TA confusions on questions or grader can significantly improve. Thanks for the course, I learnt and enjoyed the hands-on sessions.

By Ji S

Apr 15, 2018

Too coarse, quality worse than other courses in this specialization.

By Fabrice L

Aug 10, 2017

This course repeat a lot what we have seen in the module 3 of the specialization. There is not enough coding examples and the first assignment is not well design. The lectures doesn't prepare you enough to tackle the assignments.

By VenusW

Aug 25, 2017

Very disappointing course. Probably cause I have learnt text mining from other specialization, does not feel this course is necessary to take. Assignment material are poorly prepared, waste some time when completing the assignment, which can be avoided.

By Massimo A

Oct 03, 2017

Course packed of information and topics in four weeks so it feels sometimes rushed.

Especially the forth week (topic modelling, information extraction, semantic similarity and generative models all in one week) feels disconnected from the rest .

The exercises do not help too much, with several mistakes and ambiguity.

Nevertheless, the theme is really interesting. Possibly the errors can be corrected in the next runs.

Plus for using Python and NLTK.

By Alexandros B

Oct 04, 2017

poor organization of the lesson and many many mistakes during assignments

By Chris M

Aug 26, 2017

Content in the course is interesting and given the amount of data stored in text very valuable. However, I would encourage the staff to provide more coding examples. I would also suggest moving away from assignments and towards projects - (a) projects would likely force more comprehension instead of code shopping and (b) the autograder is terrible: I can't believe the amount of time I wasted because the autograder was not set up properly.

By Mark H

Jan 28, 2018

I was disappointed by the lectures in this course. My impression is that extremely complex concepts are mentioned in passsing and poorly explained, while a large amount of time is spent on trivial examples. The programming assignments are more interesting and appropriately challenging (compared to other courses in the specialization), but leave me without any confidence that I could accomplish a text mining task in python independently.

By Dan B

Jan 04, 2018

It's really unacceptable that there should be errors with the autograder (which were left unfixed) and I wasted a lot of time trying to debug code which was actually working. As well this course did a good job with the introduction to the concepts in the first two weeks and then dropped the ball with content that appears rushed and disorganized. The LDA and other concepts need to be presented better.

By Tal Y

Feb 18, 2018

The course takes you through the important NLP topics, the instruction is decent, but the assignments are clunky and waisted many hours of my time unproductively.

By Alex M

Aug 27, 2017

Instructors did a poor job of preparing students for the assignments.

By Anna K

Apr 29, 2018

Unfortunately, this is one of the worst courses I have ever taken. The later lectures did not have much of a content, and assignments were very badly described and evaluated. The latter is in general one of the weaknesses of this specialisation, but this course made me particularly frustrated. There did not seem to be any moderator answering students' questions which at least in one case led to a big confusion as one of the students wrote that his wrongly (as I got it later) written code worked ok which led to a long and misleading discussion between students how to interpret and tweak the assignment to pass the grader, which made me waste a lot of time. Would be great if wrong interpretations and statements written by students are timely deleted, corrected or flagged.

In summary, the assignments' descriptions and grading system do need to be improved (for example, one can introduce some hints such as 'the grader expected this output for this input0, but the student solution returned this' as it is done in a few other courses on Coursera).

By Cong L

Mar 22, 2018

Lecture was long-winded and could not hit the main points. Assignment was difficult without many explanation. Tutors were more humiliating students rather than providing supports.