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

4.2
stars
3,812 ratings

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...
...

Top reviews

JR

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.

CC

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!

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651 - 675 of 743 Reviews for Applied Text Mining in Python

By SHIVANGI N

Sep 10, 2020

More of practical implementation should be included

By Nilesh C

Jun 1, 2020

Topic Modelling should be explained in more detail.

By Vasilis S

Sep 1, 2018

Poor ability from lecturer to explain key concepts.

By Valeriya P

Aug 28, 2017

the course is ok, should be more technical though.

By Alonso G L

Apr 11, 2020

Very exciting topic, but not so exciting course.

By ERNESTO L C

Nov 8, 2020

Its ok but was the worst of the specialization

By 陆徐超

Dec 30, 2017

Good contents, but not very clearly explained.

By Lavanya J

Jul 5, 2020

programming assignments are too technical

By Ashwini B

Jun 3, 2018

Topics like LDA need better explanations.

By Navjyot W

Jan 18, 2020

The assignments were a little complex

By Joan P

Nov 7, 2017

A lot of issues with the auto graders

By Dhanush P

Apr 25, 2020

Last week is not properly thought

By Imran A G

Sep 24, 2018

Good for basic understanding only

By Abhijit K

Jun 8, 2020

More Hands on is required on it.

By Silvia S S

Apr 24, 2018

Assignments were too difficult.

By Georgios P

Oct 30, 2017

Week 4 was not sufficient

By Yeifer R C

Nov 25, 2018

Is difficult, but good.

By Sara C

May 16, 2018

I like the lecturer.

By Xuening H

Jan 31, 2020

Bad autograder

By pavan b

Nov 19, 2018

good training

By Aditya M

Jul 21, 2020

nice

By Alperen B O

Dec 16, 2020

bad

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 Kalashnik A

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).