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

4.2
1,930 ratings
373 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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201 - 225 of 366 Reviews for Applied Text Mining in Python

By Aditya R

Oct 05, 2017

Good Course

By Petras V

Nov 24, 2017

Very good course. A little bit heavy on in-built functions which hides what is happening underneath, but overall very good.

By Jesús P S

Jan 17, 2018

Hard course, good concepts but needs more visualization of the concepts

By Zihao H

Mar 18, 2018

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

By Dinesh D

Dec 15, 2017

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

By Fang F

Aug 29, 2017

Course is introductory and helpful. The staff should provide all the jupyter notebook code examples and provide better instruction for 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 lohith p

Aug 22, 2018

Good Material for the people who wants to start NLP. Thanks a lot for the material

By Rajat B

Aug 24, 2018

The frequent and well thought out exercises are very helpful

By Ivan S F

May 03, 2019

Great course. Worth taking it. Hard, but you will learn a lot. Homework 1 is confusing and discouraging, but pass it and the rest gets more interesting.

By Henri

Apr 19, 2019

Great course, but expect to spend a lot of time on the assignments because of errors/bugs in the questions/autograder.

By Rajendra S

May 09, 2019

Good course. But, I was expecting more depth.

By Meixian W

May 10, 2019

The course material is good and I would give a 5-star for it. The reason why I took 1 star back is that the instructor seems to be not very well prepared for this course.

First, he used 'so' too frequently while lecturing. I am not saying that he should totally not use any filler words (like 'hmm' or 'um', and 'so' is one of them), but saying that using many fillers could cause distraction and confusion. As 'so' is one of the transition words, it implies a logical connection between 2 sentences. Using 'so' a lot was actually distracting me from following the course material because I had to identify which 'so' was a filler so that I could ignore it and which 'so' was a consequence indicator so that I could pay attention to the following sentence.

Second, he sometimes seemed to get lost with the slides. For example, from Week 3 Video "Learning Text Classifiers in Python" slide at 13:36, the slide was easy to understand by showing the codes saying "NLTK.classify has something called SklearnClassifier which could let you use some models from scikit-learn such as naive_bayes or svm and here are 2 examples", but his way of explaining the slide was quite confusing. This kind of "mistakes" cost me extra time to look at the scripts to make sure that I didn't misunderstand anything.

By CHITRESH K

Apr 29, 2019

Nice introductory course to NLP , give an insight into the topic .

By Light0617

May 15, 2019

the assignment is so strange...

By Samuel O

May 16, 2019

Nice, but first assignment shouldn't be considered here I think

By Carl W

May 18, 2019

Took me into different areas. Interesting.

By Thúllio D M Z

Jun 17, 2019

The module 4 could have more hours. The key concepts are passed too fast and doesn't have a notebook with the classes content.

By Harshith S

Jun 19, 2019

What an improvement from the previous few courses. The instructor teaches much better. I could mine text in my sleep now

By Shashidhar s

Jun 28, 2019

Ultimate course for any one to start with on Data Science using Python.

By Juan M

Jun 11, 2019

a bit abstract at times.

By Linus

Jul 06, 2019

I think the course and content was interesting. I would have liked more material to look through tho. Maybe some more readings or somethings. I found specially the final week i was not feeling the help from the videos as there was so much actuall coding that was not shown or helped with in the videos. Its a tricky subject to translate the theory into the actuall code needed to finish the assignment. The final assignment took me closer to 15 houers rather than 3 as is indicated in the discription. Reading through the forum (as i spent a lot of time doing) i found that my experience seemed more normal than odd.

By Rushyasrunga K

Jul 20, 2019

Course is great except for the auto grader issues. Please look into the issue. I would like to take this opportunity and thank Prof V. G. Vinod Vydiswaran and all those who helped me to complete it.

By Christian L

Jul 25, 2019

Good course. Most part of the learning comes from personal work on the assignments (time vastly underestimated)

By TEJASWI S

Aug 01, 2019

Good course to take although I felt the course could have been better in terms of practice. But overall, would recommend to others if they wish to pursue data analysis.