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

4.2
stars
3,784 ratings

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 26, 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!

JR

Dec 4, 2020

Excellent course to get started with text mining and NLP with Python. The course goes over the most essential elements involved with dealing with free text. Definitely worth the time I spent on it.

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51 - 75 of 737 Reviews for Applied Text Mining in Python

By Aaron V

Feb 19, 2021

The course could be much better if somebody would read through the discussion comments and make appropriate changes to the wording in lab assignments.

I would not have completed the lab assignments without reading through several of the course discussion comments. Many of these comments have been there for years, yet no improvement to the course material.

By Mariana S

Nov 7, 2023

Very theoretical, with good slides but missing good Jupyter notebooks that facilitate the "hand-on" experience. Thus, it's up to the students to find out their own way to solve the assignment. Courses 1, 2, 3 of the Applied Data Science with Python Specialization are much, much better than what is offered in Course 4.

By Sudhakant P

Jun 3, 2020

Week 2 and Week 3 are OK. But Week 1 and Week 4 are Horrible. All the other courses in this specialization are amazing. But this one, I don't think so. If you really want to learn NLP, go for other sources. This course is just like a revision for the ones who are already pretty good with NLP.

By Daniel V E

Oct 11, 2023

The lectures are OK, but the assignments have very bad instructions and many bugs. Expect to spend 5% of the time learning and 95% trying to figure out what the autograder expects as an output. Overall there are much more efficient ways to learn. I can't recommend this course.

By Tin H P

Jan 15, 2023

The auto-graders are not working for quite a few questions, and error description does not tell which part of my code fails, totally useless description. Discussion forum was a big disappointment. Mentors never showed up. I felt like this is a free course, in terms of support.

By 莊子儀

Jan 16, 2024

I find that this course focuses more on theory rather than practical hands-on applications, and I don't particularly enjoy it. Also, the instructions in programming assignments are misleading or poorly worded. I have a lot of trouble ding these.

By Justin H

Nov 13, 2022

I have taken 4 courses thus far in this specialization. Course 4 is the most horrendous.

If you embrace sadistic tendencies, please take this course.

If you like to be taken for a ride and waste valuable time, please take this course.

If you like to be trolled by the instructor, its so called course syllabus and the champion of them all, the beloved autograder time and time again, please take this course.

I will specifically mention Assignment 4. The course syllabus does not even cover fully the material that will be tested in the final assignment. Its a lot of searching stack overflow and reading documentation to implement the answer. That is not all. The functions and the way it is laid out to be accepted as an answer by the autograder is ridiculous. When there are errors, the autograder doesn't even suggest what the error is. It just tells you the answer is incorrect. How does one debug? I mean.. wtf right?!

Nonetheless, I will push on and finish this specialization because it is a commitment to finish what I started.

All in all, course 4 is unforgivable. This is not the way to teach and not the way for learners to learn.

Broken. That's the only word I can think of. The course ...and me in the process.

By Elliot B

Mar 3, 2018

I found this course quite confusing and often unrelated between video lectures and assignments. The lectures maybe covered an assignment in broad strokes but to actually answer any of the questions needed extension research from the student. I felt like I was teaching myself the base content. At that point, what is the point of the lecture videos if they provide no value. I almost stopped my subscription and gave up on the data analysis specialization based on the quality of this specific course. Previous courses in the specialisation did provide useful information in lectures which was then extended upon in the assignments. This method of teaching something in the lectures then building on finessed usage in the assignments is a much better approached.

By Christopher I

Mar 14, 2018

The lectures for this course are terribly uninspired, giving very little useful information--the vast majority of it is the professor talking about obvious aspects of language at a very high and useless level. The autograder is frequently breaking for very minor things (such as returning numpy.float instead of float), the questions on the assignments are often misleading, poorly worded, vague, or just generally not very helpful. All in all, this was one of the worst MOOCs I have ever taken, though the Coursera bar is pretty low. It does make me wonder why I bother to pay at all--oh right, Coursera now makes not paying a major inconvenience to course progression.

By Guo X W

Jun 21, 2020

This is my least favourite course in the specialisation. Natural language processing is an exciting field and I think there is a lot more potential to enthuse and engage students. The instructor scratches the surface of text mining by going through brief sets of codes on ppt slides. I thought it would be meaningful to use more real-world datasets (as in the previous courses in the specialisation) and have students follow through some examples on Jupyter Notebook. I also felt that the exposition by the instructor was not the most intuitive or lucid. It could be much clearer.

By Will W

May 21, 2021

While containing marginally less bugs and errors than previous courses in the specialization (though they are still present and bafflingly U of M shows no interest in ever fixing them), this course spends too much time going over topics already covered in previous courses, then shifts to a very simplistic and rudimentary overview of the material at hand. The last week especially is so rushed that I don't feel I have a solid grasp on what was supposed to be taught, and the assignment is just paint-by-numbers reading of documentation and plugging in guesses.

By Matt P

Apr 11, 2020

This course was much less helpful than others in the Specialization. The assignments are poorly conceived, and submissions are beset by finicky autograder issues. Certainly, data cleaning and code debugging are critical skills for text mining, but I find it difficult to believe that "try to understand what output a function should submit so as to satisfy the current autograder" is a useful way to teach text mining.

I hope this course will be re-done to bring it in line with the quality of the others in the Specialization.

By Denys P

Aug 10, 2019

The course is a joke. Its outdated and not supported, you literally need to spend hours to try and figure and emulate versions used by autograder and even the file structure for files used by default is not accurate and you get file read errors on predefined by them functions on their own virtual environment and need to fix these for them!!! The virtual machine env provided is super slow so need to use your own. Very bad user experience and horrible use of time!

By Daniel W

Aug 27, 2019

What a horrible course. Especially the assignments are such an unbelievable waste of time. Instead of focusing on important concepts and applications, one has to spend hours one "pleasing the autograder" by renaming columns and reading the discussion pages for the correct interpretation of all the ambiguously formulated questions. Very sad! Would be good for everyone if this was removed from the (otherwise great) series "Applied Data Science in Python".

By Eduardo C F

Feb 23, 2018

I was under the impression that the course is incomplete, especially week 4, which has no notebook examples of the theory presented. I needed to look at other sites for basic information. I could only complete the exercises because they are easy, otherwise, with the code presented during the course, I would not have been able to. I suggest strengthening the example code in python (see week 3, good code)

By Ginger d R

Jun 27, 2023

Do yourself a favor and do the NLP specialization from deeplearning.ai. This course (and specialization in general) is outdated. Assignments are poorly organized and the poor moderators can only warn you about assignments as they're unable to change anything. After taking Dr. Chuck's & Dr. Resnick I was seriously dishes out some cash on the online Master's they're offering. Glad I didn't...

By Rubén G C

Apr 27, 2020

I have to say that the previous three courses were very well explained, with good examples and python code. However, this course is not well explained nor documented. It is a pity that the quality of the whole specialization program gets considerably reduced due to this course. The assignments do not allow you to learn and you may not pass them due to small differences in the coding.

By Justin M

Sep 14, 2019

Videos are so high-level that they don't help at all understanding the necessary code. Assignments have spelling errors and ambiguity. Week 4 is missing the sample code notebook. I eventually found the sample code notebook in the forums, but this was a big cause of frustrations as I had zero context for how to do the assignment.

By Daniel B

Dec 28, 2020

I don't feel like I learned anything even though I passed with 100%. This course desperately needs more insightful quizzes and assignments, and the lectures should actually explain how to do "applied text mining" rather than just glossing over some terminology.

By Nicholas P

Jul 31, 2019

Unless the instructional staff updates the programming assignments to reflect updates in packages and ensures they can run without additions, do not take this course. It is a terrible reflection on the University of Michigan.

By Didier C

Apr 21, 2020

The subject is interesting however the lectures are too shallow and the assignments too difficult. You should be expected to do more study after the lecture for sure but for this course, it was a lot.

By Manoj B

Jul 14, 2020

Not a great course. I'd skip it. The assignments were just trying out different parameters. Nothing related to machine learning/using Python was discussed in the class (may be 2%). Didn't lean much.

By SHAHAPURKAR S M

May 26, 2020

Video lectures are just been run through. No clear explanation at all. On the top of that, assignments are freaking difficult being totally irrelevant to the material taught in the video lectures.

By Mark R

Sep 21, 2017

Interesting topic, but a really poor course with barely any content.

Around an hour or less of lectures a week.

I've taken a lot of MOOC's on Coursera and other platforms and this one is poor

By Jean-Michel P

Jun 2, 2021

After 4 courses in the stack, this one is the worst. My tip would be to look on youtube for a tutorial of the topic at hand and skip the UoM lecture.