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

1,985 ratings
382 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


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. :-)


Jun 26, 2018

Would love to see these courses have more practice questions in each weeks lesson. Would be helpful for repetition sake, and learning vs only doing each question once in the assignments.

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301 - 325 of 374 Reviews for Applied Text Mining in Python

By Carlos F P

Oct 16, 2018

Autograder is a disadvantage that sometimes can take many hours to figure out. Also, this course was a let down to the previous in the specialization. I wish there were more examples.

By Jeffrey D B

Oct 16, 2018

The course would be significantly improved if there were more hands-on demos during the lectures. Lectures are very high-level and aren't terribly useful when trying to do the lab exercises.

By Imran A G

Sep 24, 2018

Good for basic understanding only

By Eric S

Sep 27, 2018

Most assigmets were not in the notes. Still everyhting seems really usefull.

By 陆恩哲

Aug 30, 2018

I don't think the lecture is very clear. Although I have finished the homework, I still feel a little confused about the concepts.

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 Vishal S

Jul 16, 2018

Lectures are good but the assignment of week1 and week 4 is a little bit absurd and unclarified. Autograder is too slow.

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 Andre N

May 12, 2019

Good course, however lots of problems with assignment notebooks not working the way they should


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 Pengcheng Z

Jun 19, 2019

Overall the course structure and assignments are very good. But need too much extra effort to finish homework. The course video itself may only covered 20% of content so a lot of extra times is required for me to finish homework. Some of the effort from my perspective is not necessary. From my perspective If the course could cover 70% of the content while push student to explore the remaining 30% it would be more efficient and encouraging.


Jul 08, 2019

Contenuti troppo superficiali, gli assignment troppo specifici

By Izabella J

Jul 28, 2019

It's the worst course in this specialization. Still it's OK, but... You get a lot of things during the lecture which are not connected to the Notebooks. Notebooks are the poorest I saw here. Assignments have some errors in code (you need to add download part for example to run the Notebook, or change folder where data are). But the content is interesting, I learn quite a lot doing assignments. But I still feel disappointed, as other courses were much better and I was expecting more.

By Juan V P

Jul 29, 2019

I think the course need a better speaker and more notebook hours to be enhanced.

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 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.

By Alexander W

Sep 03, 2019

This course is interesting and about a very important topic, but it urgently needs an update!

By Vivek G

Sep 19, 2019

Only useful for coarse understanding of the topic.

By Farzad E

Mar 18, 2019

It gives you a better understanding of SVM and LDA after taking the third course but they have failed to provide enough examples and exercises. Not every module has a notebook unfortunately!

By Bruce H

Jul 30, 2018

One of the more disappointing classes in the U of M data science specialization, due mostly to inconsistent quality of the assignments. The videos are interesting but lacking in detail. The quizzes are trivial. Half of the assignments were OK but the other two were big time-wasters. The construction of this class seems just plain lazy. Proceed directly to google and skip this class.

By Fabrice L

Aug 10, 2017

This course repeat a lot what we have seen in the module 3 of the specialization. There is not enough coding examples and the first assignment is not well design. The lectures doesn't prepare you enough to tackle the assignments.

By Tal Y

Feb 18, 2018

The course takes you through the important NLP topics, the instruction is decent, but the assignments are clunky and waisted many hours of my time unproductively.

By Dan B

Jan 04, 2018

It's really unacceptable that there should be errors with the autograder (which were left unfixed) and I wasted a lot of time trying to debug code which was actually working. As well this course did a good job with the introduction to the concepts in the first two weeks and then dropped the ball with content that appears rushed and disorganized. The LDA and other concepts need to be presented better.

By Cong L

Mar 22, 2018

Lecture was long-winded and could not hit the main points. Assignment was difficult without many explanation. Tutors were more humiliating students rather than providing supports.

By Oliverio J S J

Feb 13, 2018

This course provides an interesting introduction to natural language processing in Python. The lessons are well thought, they are brief and to the point. It is very exciting to discover all the tools at our disposal to work in this field. The main problem of the course, as it seems to happen in the whole specialization, is resolving the assignments. Usually, they are poorly described, which forces the student to review the forums to understand what they are asked to do. In addition, the part of the tasks related to the course's topic is usually very simple, sometimes trivial. On the other hand, several hours may be required to generate the specific data structures required by the autograder an dealing with weird issues, that is, much more time is devoted to deal with autograder problems than learning about the subject. I do not understand why this problem keeps repeating one course after another.