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

3,224 ratings
610 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


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!


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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426 - 450 of 602 Reviews for Applied Text Mining in Python

By Siddharth S

Jun 12, 2018

The fact that the strategy of a Jupyter Notebook Demonstration during explanation was not followed in week 3 and 4 was a disappointment.This specialisation had been wonderful with its use of demonstration in Lectures with the Notebook,If this had been followed in Week 3 and Week 4 then the course would definitely had shined.Please correct the same, the course deserves that, It has wonderful content.

By Vinamra B

Jun 11, 2020

It was okay. Not as good as rest of the courses. I expected to learn more but was little disappointed. It would have been good if little more explaination was given for the functions that were used to do the tasks. But was Satisfactory. I would recommend you to take the course, but you might need to learn from different sources also to develop a knack in the topics of this course.

By Anand N A

Nov 17, 2019

This course contents was good, but the assessment was really bad. You guys need to fix the autograder issues ASAP and I feel the instructor was not taking as much care as others to set the autograder propertly. Lot of time was unnecessarily wasted I would say as the instructions could have been better. Very disappointed with the assessment. Course is very useful and valuable.

By yiding y

Aug 24, 2018

The assignments in every week are valued practice to get familiar with the knowledge in text mining. For example, regular expression, sentiment analysis, semantic similarity, LDA topic modeling and so on.

However, the videos are sometimes confused and less organized. It could be better if having more details or at least sharing more reading related materials.

By Matthew O

Dec 06, 2019

I found this course the least valuable of the courses in the specialisation so far. The video content wasn't quite as slick/informative, the assignments not quite as useful or well worded, making them ambiguous in a few places and generally it just wasn't quite as good. Not terrible, but just not quite up to the high standards of the other courses so far.

By Yulo L

Jan 16, 2018

The course Assignments could be more clear and consistent with what is actually taught in the class. A good example is when n-grams were required to calculate the similarities, but have actually not been introduced in the video yet.

Also, an expected answers would be nice for the assignments.

Other than that, it was a nice introduction to NLP in Python.

By Brian R v K

Oct 30, 2017

I enjoyed this course, but some aspects of it felt "light touch", particularly week 4. That week would be greatly improved with a jupyter notebook and an applied demonstration by the absolutely awesome Teaching Assistant, Filip Jankovic. Whenever he does a demonstration, it's clear, concise, practical, and always helpful. Let's see more of him!

By Cathryn S

Jul 01, 2020

A light introduction to a big topic. The exercises are what make it worthwhile, and take most of the time. I spent 10-15 hours on each 'week' about 2/3 on the exercises.

Be prepared to do a lot of your own research and reading - there are very few readings provided and you'll need to use blogs and other resources to fill out your knowledge.

By Joshua B

Aug 03, 2019

Professor was great and gave engaging and interesting lectures but the course materials were lacking both in maintenance and definitley could have been more in depth. However, one of the mentors (Uwe) was very helpful in his forum posts which made some of the deficiencies in the assignments less of an issue for me. Thanks Uwe!

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()).

By Rasoul N

Dec 26, 2017

Course materials are amazing but there are not much support for assignments.

I did all the quizzes and assignments except the last one. It seems there was something wrong with auto-grader or the assignment was not clear. There were complains about this issue on the forum but no one from staff answered the questions.

By Greg S

Apr 13, 2019

I found this course to be a good introduction to NLP. The lectures where fine as such, but lacked in technical focus making it difficult to tie them to the homework. I expect this is the style of the professor. The homework problems where good, but you do need to work to put it together with the lectures.

By Mark M

Aug 20, 2017

This is the 4th one and also a very important building block in the data science specialization. However comparing to the other courses there is much talk from the lecturer and not so much of interesting background information of this topic. So this course does not go far beyond a good tutorial.

By Yahia K

Mar 24, 2018

It is an interesting course. The difficulty level is a bit high if you have never worked with text data before. The later assignments are not structured very well and in some cases the auto-grader has issues that cause correct answers to be marked incorrect. Overall, I got some use out of it.

By Gabriele L

Jan 19, 2018

The videos are very good, the teacher is clear and concepts are explained well. The assignment are frustrating because of the misunderstanding that could arise due to the nature of the assigments themselves. Exercise are not explained well.

Overall it is a good course, I would do it again.

By Kerem Y

Feb 06, 2020

I liked the previous courses in the series better. I think this course did not have enough "meat on the bones"; the ML method descriptions were generic and already seen in previous courses. Would have liked seeing more explanation how this all works in context of text and text mining etc

By David B

Aug 15, 2019

This course teaches basic, practical skills for text mining with Python's regular expression (re, pandas) and NLTK package. While the lectures do not go into much depth and are typically too slow or too fast, the assignments are good exercises for learning basic text mining techniques.


Feb 11, 2019

I will not say that I did not learn anything. I just wish the autograder was a little better. Basically, quite frustrating to fight a black-box grader. An example of a better autograder is the one implemented by the Princeton people for their algorithm courses.

By Mike W

Nov 12, 2019

Compared to other courses, there's a disconnect between what's covered in the lecture and what's needed to complete the assignments; the lectures at times have a more theoretical flavor. For a course with "applied" in the name, that's a more significant mistake.


May 19, 2019

In general, the course is good, lesson explanations are excellent but it lacks of pratical lessons. Assignments are quite difficult in comparison with the material of the course lesson. It took me a lot of time to do them and understand where my mistakes were.

By Maxime R

Mar 13, 2018

I really think that the 3rd and 4th week of the course should have more practical presentation (especially the 4th week for which the assignment is quite 'new' in terms of programming). Having a notebook for the 4th week would be a good additional material.

By Daissy D M R

Feb 19, 2019

Good topics and well explanations. A Notebook to support content of week 4 is definitely needed. More explanations in assignment for week 4 is needed. In general, week 4 lacks of organization and good content. that is why I give 3 stars instead of 5

By Jack O

Jul 24, 2018

I don't feel like I learned very much; even a month later, I've almost entirely forgotten what we covered. The homeworks were confusing and often poorly worded, and from what I saw from the forums, I wasn't the only one who was left baffled.

By Joseph I

Jan 28, 2020

The videos and content were great but the projects need more specificity. There's a lot of ambiguity around what the projects are asking for which takes away from the quality of the course. For examples, please visit the discussion forums.

By Muhammad H R

Feb 13, 2018

This course was just too theoretical. There were just too many lectures on the English language and nothing really practical. I learned nothing that I can actually use. There were hardly any useful text mining techniques that I learned.