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

4.2
2,165 ratings
409 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

CC

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!

GK

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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176 - 200 of 401 Reviews for Applied Text Mining in Python

By Michael M

Mar 22, 2019

Great course. Auto graders have some issues.

By Frank L

Oct 14, 2017

Was very detailed and well taught

By Patrick L

May 26, 2018

Great course, didn't expect to get so much value out of it. Sometimes the instructions in the assignments are unlear though.

By Aleksey B

Mar 08, 2018

Some assignments were very ambigues - namely part One of final assignment.

By Nicolas B

Aug 26, 2017

Great course, could improve the last week with more practical examples.

By Christos G

Aug 22, 2017

This was a very well thought and assembled set of Text Mining applications in Python. The complexity and profoundness of the topic somehow prohibited the instructors from sufficiently explaining the details in some occasions, which might eventually cause frustration with the students. However, perhaps this wide-first approach versus the deep dive is preferable for the purpose of the course. In all cases, Google and Stackoverflow will always remain as last resorts and supporting information sources.

By Sebastian H

Mar 18, 2018

The course is quite interesting and you learn the basic concepts and tools.

The programming assignments were sometimes unclear in the formulation of the tasks. Additionally the autograder seems to be a bi buggy, which was very frustrating and cost me a lot of time.

But, thanks to the vivid and helpful discussion forum in the end it is feasible.

And since you learn the most out of all this little hurdles ;) , the course is still very valuable!

By Fedor K

Nov 16, 2017

G

By cheting c

Jun 22, 2018

Overall good class but the assignment design is somehow bad. Should give some hint otherwise we do not know if we get a wrong answer or the autogarder is not working.

By Saber D

Jan 17, 2018

Very useful course to text mining.

By Aditya R

Oct 05, 2017

Good Course

By Kunal c

Aug 10, 2017

There were certain issues with the autograder. But the course content was good

By Jeremy L

Oct 26, 2017

The course itself is good, but the assigment system is not robust and some sentences are also ambiguous to users. Seeing from the forums, many users get confused in the assigments.

By Dongliang Z

Jan 13, 2018

wk1-wk3 are good. w4 is a little weak to build the connection between texting mining and coding. Moreover, it will be more straightforward if the lecturer teaches more about the procedure to deal with text mining. I just passed this course but don't master text mining technique through it.

It is still a good introduction to texting mining, a very beginning of it.

My suggestion is that wk4 should be reconstructed to make people really believe they can use what they learn in this course after they pass the assignments.

Finally, thanks the lecturers for introduction. Especially thanks all students who contribute a lot in forum. Without them, I cannot pass the assignments.

By Vinicius G

Dec 26, 2017

I did not give 5 starts because Assignment 4 was really hard. It required too much knowledge from outside. The week 4 modules poorly prepared me for assignment 4.

By Anad K

Jun 17, 2018

Content-wise course is okey. But all the students faced issues during the assignment submissions because of the auto grader.

By Чижов В Б

Dec 18, 2017

It is interesting, cognitive and very useful. But, there were very few answers from the teaching staff in the discussions at the forum. In previous courses of this and other specializations, the teaching staff took an active part in the forum and this greatly helped in understanding and fulfilling the tasks of the course.

By Mykhailo L

Jan 03, 2018

Good intro course on NLP

By Aditya h

Jul 09, 2018

Great course! very much handy if you are looking for a 'Text processing in Python' primer. The good thing about the course is that it explains the libraries. For example - NLTK vs SciPy for applying ML on text. What's missing, is the Deep Learning aspects of text processing

By Beda K

Aug 27, 2017

Good introduction into the field of text mining, but very brief. I think the structure could do with some fine tuning as for example the extraction of features from text is left mostly untouched or is covered by the home work only. All in all I found it slightly less well structured than the previous parts in the series, but it was still very useful and helpful as a starting point.

By Srinivas K R

Sep 16, 2017

A good course which introduces you to the basics of text processing and text mining in python and exposes you to tools such as regex, nltk and gensim. While the lectures and assignments do promote this learning, a lot of the criticism that is directed at the course is due to the auto-grader issues. You can easily side-step a lot of these problems by going through the forums. However, I do think that the course could have been better planned and executed, even IF the only purpose is applied text mining for e.g., better context and some exposure to theory or at least pointers to where more material could be found for self-study would have been helpful. However, I did learn some things from the class giving me a push towards learning more on the subject on my own.

By Pankaj K

Jan 07, 2018

Great material with practical applications! I utilized a lot from this course in my work! I think the assignments should be made a little bit more clearer, specially the first one. Took a lot of time to do the first one, due to some exceptions that were not mentioned in the exercise, at least one should mention that there might be cases other than specified here.

Overall a great course! Thanks!

By Mauro G

Oct 03, 2017

The lectures are in my opinion too concise. The programming assignments are very interesting. Perhaps the week 1 programming assignment is too complex.

By Traci L J

Dec 07, 2017

I learned a lot about regular expressions, how to use NLTK to parse words and parts of speech, and to apply machine learning techniques from the third course to text.

The homework assignments were finicky with the autograder and often there was a lot of frustration regarding the exact data types of the output. I spent a lot of type debugging over simple things that could have been clarified in the assignment description. However, the discussion forums are active and people are willing to give feedback!

By Oscar J O R

Sep 02, 2017

Nice introduction to the topic and interesting tools. The evaluation system could be improved adding more resources focused on the use of the nltk functions or giving some advice about the critical points in the Python demonstrations.