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

3,479 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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426 - 450 of 659 Reviews for Applied Text Mining in Python

By chetan s

Apr 23, 2020

Good course to start with NLP

By Juan M

Jun 11, 2019

a bit abstract at times.

By Mischa L

Jan 3, 2018

Good intro course on NLP

By Eric G

Aug 20, 2019

The autograder sucks!

By Ankit G

Nov 18, 2018

basic and nice course

By Christian E

Mar 27, 2019

Very good content

By Victor G

Nov 6, 2018

Very interesting

By Patrick L

Nov 27, 2019

It needs update

By Liran Y

Apr 7, 2018

Great Content.

By Yang F

Aug 22, 2017

Useful topic.

By shubham z

Jun 13, 2020

good course

By aditya r

Oct 5, 2017

Good Course

By Chen G

Nov 5, 2019


By Harsha V M V

Sep 16, 2020

Good one

By Lalit S

Jan 29, 2019


By Sweta c

Aug 20, 2020


By Rahila T

Nov 15, 2018


By Utkarsh T

Dec 18, 2018


By Alex F

Feb 24, 2020


By Fedor K

Nov 16, 2017


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 Steve M

May 3, 2018

The content of this course has great potential, but needs significant refinement. The lectures, while delivered with enthusiasm, were very theoretical/academic and provided little in the way of preparation for the more practical exercises. The disconnect between lectures and assignments, coupled with technical challenges (autograder glitches) were frustrating. The only support came from one dedicated volunteer Coursera Mentor; the instructor cadre was absent or unavailable to students throughout the four week period. The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.

By Samuel E

Oct 1, 2017

The grading system is supremely messed up and at least I have a vague idea what am talking about because I have completed more than a dozen coursera courses. Also, the methods used through the courses teaches very bad coding approach relying on global variables.

Below is an example from Module 2:

def example_two():

return len(set(nltk.word_tokenize(moby_raw))) # or alternatively len(set(text1))


Why would they not pass moby_raw and text1 as arguments in the function?

With that said, the course could easily be one of the best intro NLTK courses out there minus the frustration and very poor design.

By Ben E

Nov 10, 2017

This course did cover some good topics (Naive Bayes model, similarity, part of speech tagging). However, I felt the homework was more about manipulating Python data structures than learning anything significant about text mining. Some of the theory behind the models was covered, but didn't make it to the homework.

It would be difficult since this is a short class, but I would have preferred more about tips on which model to use and feature engineering / selection, and examples of practical applications of text mining. (Or stories of failures in the instructors' experience!)

By Wenlei Y

Nov 19, 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.