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

3,478 ratings
668 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 26, 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!

Dec 4, 2020

Excellent course to get started with text mining and NLP with Python. The course goes over the most essential elements involved with dealing with free text. Definitely worth the time I spent on it.

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626 - 650 of 659 Reviews for Applied Text Mining in Python

By naive666

Jun 29, 2019

Far from expectation, feel upset

By Elliot B

Mar 3, 2018

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 Christopher I

Mar 14, 2018

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

Nov 18, 2017

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

Jun 21, 2020

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

Apr 11, 2020

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

Aug 10, 2019

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

Aug 27, 2019

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

Feb 14, 2020

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

Feb 23, 2018

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

Apr 27, 2020

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

Sep 14, 2019

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 Daniel B

Dec 28, 2020

I don't feel like I learned anything even though I passed with 100%. This course desperately needs more insightful quizzes and assignments, and the lectures should actually explain how to do "applied text mining" rather than just glossing over some terminology.

By Nicholas P

Jul 31, 2019

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

Apr 21, 2020

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

Jul 14, 2020

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.


May 26, 2020

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.

By Mark R

Sep 21, 2017

Interesting topic, but a really poor course with barely any content.

Around an hour or less of lectures a week.

I've taken a lot of MOOC's on Coursera and other platforms and this one is poor

By Sean M

Apr 17, 2021

lectures didnt cover material in assignments specifically week 4. lead to a lot of supplementary research and headache.

By Luis d l O

Nov 20, 2017

Too simple. Few information and content, and extremely simple (though with a lot of problems) assignments.

By Dario M

Jul 19, 2019

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

By Akshat S

Mar 26, 2020

The NLTK library was not explained properly. No code explanation was provided.

By Zhongtian Y

Aug 2, 2020

Teacher does not explain well and lectures are not detailed.


Nov 26, 2020

Horrible assignment and horribl week 3 and 4. Please avoid!

By Jose Á P L

Mar 23, 2019

Este curso no vale para nada, por favor no lo hagais!!!