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

4.2
2,131 ratings
403 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 396 Reviews for Applied Text Mining in Python

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 Gunjari B

Jun 18, 2018

Lecture materials are not comprehensive enough to solve the assignments. Course is dependent on precursor courses in the specialization. Assignments often require reference from upcoming weeks. lectures are inadequate. The course is average at its best

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 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.

By Leo C

Feb 17, 2018

Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.

By Liran Y

Apr 07, 2018

Great Content.

By Niels R

Apr 24, 2018

Overall this is well done course, but the autograder for week 2 needs a lot of work. It was buggy, broken and gave uninformative answers.

By Girish G

Nov 20, 2017

Awesome course..It is a good start for NLP. Comprehensively covers all topics. The Autograder for the programming assignment needs

By Iurii S

Feb 10, 2018

overall a goods intro into text analysis

By João R W S

Aug 23, 2017

Very good course with very good material and teachers. I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).

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 Zihao H

Mar 18, 2018

The final assignment is not well worded, and answer for the autograder is too strict.

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 Dinesh D

Dec 15, 2017

Course material was good but week 4 assignment set up is a disaster.