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

4.2
stars
3,784 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 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

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!

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.

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651 - 675 of 737 Reviews for Applied Text Mining in Python

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).

By Oliverio J S J

Feb 13, 2018

This course provides an interesting introduction to natural language processing in Python. The lessons are well thought, they are brief and to the point. It is very exciting to discover all the tools at our disposal to work in this field. The main problem of the course, as it seems to happen in the whole specialization, is resolving the assignments. Usually, they are poorly described, which forces the student to review the forums to understand what they are asked to do. In addition, the part of the tasks related to the course's topic is usually very simple, sometimes trivial. On the other hand, several hours may be required to generate the specific data structures required by the autograder an dealing with weird issues, that is, much more time is devoted to deal with autograder problems than learning about the subject. I do not understand why this problem keeps repeating one course after another.

By Jonathan B

Jul 14, 2020

Text mining and NLP were areas of this specialization that I was particularly interested in learning more about and I was mostly disappointed by the course. The staff's refusal to update to the latest versions of software is frustrating because being successful in this industry means staying up with the latest trends. I recall at least one lesson that required Python 2.x, which as of 2020 is no longer supported.

While it is completely understandable that assignments include some concepts that were not taught in the lectures; this course had way too many self-learning concepts in the assignments many of which were covered in the very next lesson.

On a good note, the instructor is very passionate about the topic and covers a lot of material. The course mentors are very knowledgeable and helpful and there is no way I would have been able to pass the course if it wasn't for them.

By Tobias K

Jan 29, 2022

I was very much looking forward to the course on text mining, but my enthusiasm has been dampened as the course was not designed particularly well. The hands-on examples are relatively short and stayed on a superficial level. They did not prepare students sufficiently for the assignments, which were pretty buggy and felt like a wild goose chase at times. On several occasions, I seriously considered breaking the honor code and looking up the right answers, as this would have given me the time and opportunity to actually learn something new on LDA and similar topics from online tutorials that are available on other sites in the internet. Having said that, I still learned something in this course, but what I learned clearly remained below my expectations.

By Ryan D

Aug 6, 2019

I have been working through the entire specialization, Applied Data Science with Python. The first two courses of this specialization had a lot of attention to detail, the assignments were well laid out and challenging, and the addition resources linked by the instructor were really helpful. Moreover, the lectures themselves were more engaging and segmented.

This course was less informative than the other courses I've taken in this specialization. You would be much better off purchasing the O'Reilly Text Analysis in Python book and reading through it in more detail prior to taking this course or in-between lectures.

By Stan S

Mar 18, 2022

This course is much lower standard in comparison to the rest of Applied Data Science series. There are significant issues:

1. The concepts are covered either at very high level or completely missed

2. Assignments are of mechanical nature and lack the link with real life

3. Poorly written assignments - key info missing and unclear

4. Auto-grader is very picky and will require loads of ours of troubleshooting

I would not do this course unless you have to as part of the specialization.

If you do make sure to read discussions before attempting assignments- trust me it will save you a lot of trouble