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

4.2
2,114 ratings
397 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

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. :-)

CB

Sep 20, 2017

Excellent course! Video lectures are high quality, with realistic problems and applications. Exercises are reasonably challenging, and all quite fun to do! Strongly recommend this course

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226 - 250 of 390 Reviews for Applied Text Mining in Python

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 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.

By Aswath M

Aug 02, 2019

Excellent course for someone like me who is ambitious and aspires to gain knowledge on new things. The videos can be made bit more elaborate, seems to be rushing towards the end.

By Manuela D

Aug 08, 2019

Well thought, very basic level, but a good starting point.

By Daniel J

Aug 07, 2019

It is quite a dense topic, however the instructor manages to make it much simpler.

By Ahmad H S

Aug 14, 2019

the course is good, but more practices is required

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 Eric G

Aug 20, 2019

The autograder sucks!

By Alireza F

Sep 16, 2019

I believe that assignments are away harder than the material of the course. The instructor should more get involved in the codes when he teaches.

By Alan H

Sep 26, 2019

The course provided a good overview of basic text mining for people who are brand new to NLP. The problem is really in the quality of the assignments. The quizzes are really simple and the programming assignments have many errors and provide no feedback for debugging. If it wasn't for the forums and the awesome mentor Uwe (who answers everyone's questions!), I would not have been able to complete. I felt like I learned a good amount, but in a painful way

By Vidya M S

Sep 30, 2019

A good brief introduction to test mining with python. The professor attempts to explain the topics well. Good rigor of the assignments. How ever for the last module , absence of explaination with a notebook is strongly felt as the concepts get deeper in understanding and woud have helped with the last assignment.

By Kangqiao

Nov 04, 2019

A little bit stretched my python skill, but learned a lot. Forum is a good place, and maybe next I will join some study group online or offline to have more discussions.

By Chen G

Nov 05, 2019

Helpful..

By pavan b

Nov 19, 2018

good training

By Stephane C

Dec 09, 2018

Week3 and 4. Too much of strange bugs with the auto grader. Not enougth examples...

By SeyedAlireza K

Dec 23, 2018

I learned some useful stuff in this course but I think it could be a little more deeper and teaching more behind theorems especially for week 4.

By Mateusz M

Feb 06, 2019

Some of the topics where elaborated very briefly. There was not enough practical examples and instructor was no clear in what he was saying.

By Craig A B

Nov 19, 2018

You do more work learning on your own to be able to do the projects and quizes then is given in the lectures. These University of Michigan classes aren't very balanced in terms of lectures, reading, and difficulty of projects.

By Yeifer R C

Nov 25, 2018

Is difficult, but good.

By Avi A

Jan 17, 2019

Great instructor, but the assignments are a big jump from the course notebooks in terms of difficulty. I also faced numerous issues with the autograder. In the last module, there were wrong pieces of code in the notebook and module (like ROC score being calculated from model.predict() instead of model.predict_proba()).