Chevron Left
Back to Applied Text Mining in Python

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

4.2
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

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. :-)

BK

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.

Filter by:

326 - 350 of 374 Reviews for Applied Text Mining in Python

By Chris M

Aug 26, 2017

Content in the course is interesting and given the amount of data stored in text very valuable. However, I would encourage the staff to provide more coding examples. I would also suggest moving away from assignments and towards projects - (a) projects would likely force more comprehension instead of code shopping and (b) the autograder is terrible: I can't believe the amount of time I wasted because the autograder was not set up properly.

By Alexandros B

Oct 04, 2017

poor organization of the lesson and many many mistakes during assignments

By Pascal R

Oct 08, 2017

First Coursera course I've taken with mistakes in the material and the grader. Also the first course where they mostly decided not to provide notebooks to review the material but instead made you scrub through the videos to find the actual code. Lastly the assignments were not terribly well tuned to the lectures (which were decent) and didn't make me feel like I had a great grasp of the material.

By Saravanan C

Aug 12, 2017

Liked the simplified content. But minimalist approach w.r.to coverage of concepts - could be better. Tactical/Operational support, responsiveness from the TA w.r.to confusions on questions or grader can significantly improve. Thanks for the course, I learnt and enjoyed the hands-on sessions.

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.

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 Raul M

Jun 02, 2018

I didn't like too much the structure of the lecture and the assignments, I don't think they were aligned that well. Also, I'm not sure how I'm going use this in real life.

The additional lectures were TOO MUCH theory which is not the purpose of the specialization.

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 Ji S

Apr 15, 2018

Too coarse, quality worse than other courses in this specialization.

By Alex M

Aug 27, 2017

Instructors did a poor job of preparing students for the assignments.

By Anna K

Apr 29, 2018

Unfortunately, this is one of the worst courses I have ever taken. The later lectures did not have much of a content, and assignments were very badly described and evaluated. The latter is in general one of the weaknesses of this specialisation, but this course made me particularly frustrated. There did not seem to be any moderator answering students' questions which at least in one case led to a big confusion as one of the students wrote that his wrongly (as I got it later) written code worked ok which led to a long and misleading discussion between students how to interpret and tweak the assignment to pass the grader, which made me waste a lot of time. Would be great if wrong interpretations and statements written by students are timely deleted, corrected or flagged.

In summary, the assignments' descriptions and grading system do need to be improved (for example, one can introduce some hints such as 'the grader expected this output for this input0, but the student solution returned this' as it is done in a few other courses on Coursera).

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 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 Stephen

May 03, 2019

Out of the 5 courses in the specialization, this course was not up to the level of the other courses. Full of theory not much practical explanations and there wasn't much practice modules in each week just like other modules.

For Assignments, i was not even able to refer any module in this course to check for syntaxes. It was very tough for me to solve assignments as there was no reference in the videos or practice modules.

Need huge improvements in the course.

By Matt K

Apr 17, 2019

The overall material was good. That being said, this is the first time I have taken a MOOC course and felt like 90% of the time I spent was fighting with the auto grader. The instructions in many instances were unclear, so when you are dealing with a grading system that grades items as 100% correct, vs 100% incorrect with really no feedback as to what you did wrong it can be very frustrating. Without the Discussion forums there is no way I would have ever figured out what to do for some parts of assignments.

By Vincenzo T

Apr 27, 2019

Course is very interesting. However, getting your assignments right is extremely annoying. Sometimes you have no idea why it's not right. Every upload you need to change the type of your upload.

By Gonzalo P

May 14, 2019

Los ejercicios de este curso tienen una dificultad muy superior a lo mostrado durante las clases, lo que hace que uno deba de invertir mucho tiempo en los mismos investigando en recursos internos. Por ejemplo, con una dedicatoria semanal de 10-15 horas me llevó 2 meses enteros hacer el curso.

By Stanley C

May 15, 2019

Assignment grading is way too rigid and not reflective of real world issues. It can be very frustrating.

By Sebastian

Apr 30, 2019

The video lectures are good, but there are many issues with the Jupyter notebook assignments.

By Shikhar S

Jun 06, 2019

The content of the course was quite good. But the level of teaching was a way too less than the level of Assignments. Ist assignment was too difficult to perform..

By Samuel K

Jun 22, 2019

Good course with great content and lecturer however the assignments are all buggy and don't run in the Jupyter notebooks. This is frustrating to deal with in a paid course. Please fix!

By naive666

Jun 29, 2019

Far from expectation, feel upset

By Lin Y

Jul 16, 2019

This course is probably the worst amongst all other courses in this specialization. The term 'applied' in the course title makes you think that this course helps you to build practical experiences in text mining. However, not true at all.

By Svitlana K

Jul 29, 2019

Worst course in the specialization so far. Tasks in the assignments are very poor written and are unclear. Just listening lectures don't help you to complete your assignments.