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

4.3
stars
3,474 ratings
668 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

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.

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!

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476 - 500 of 659 Reviews for Applied Text Mining in Python

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 6, 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.

By Frank Z

Feb 19, 2021

The first two weeks are great. However, week 3 and 4 are not that great. There are not many examples in week 3 and week 4, however, the assignment requires extra understanding of the models. Also, the instructions in the assignments are ambiguous. I hope the materials could be updated.

By CMC

Feb 10, 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.

By Janick R

Oct 23, 2020

The topic and the concept are really helpful, but the way they are taught is not that good. I also had some problems with the assigment; my answers were correct, but the algorithm wasn't, so it told me I had something wrong and the staff doesn't help too much.

By MANCINI L

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 18, 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.

By Jaerong A

Jun 21, 2020

The lectures are pretty fast-paced, and the assignments expect you to do things that are not well covered in the lecture. You need to learn a lot by yourself to learn anything from the lecture. Besides, autograding is a disaster.

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 Mayeul P

Nov 6, 2017

Great teaching material and clear explanations. I learnt a lot.

Nevertheless the assignments auto-grading tool is awful.

I spent more time looking for the necessary hacks to pass the assignments than working on text mining.

By Ruiqi Y

Apr 26, 2020

The content of the video and the assignment needs to be updated. Some of the questions in the assignment were not clear and autograder can be a pain. The topics are also not too coherent from weeks to weeks.

By Tianyi C

May 15, 2020

The assignment is very easy compare to the previous three courses, just need to apply some library and done. The syllabus is poorly designed, especially for the last two weeks. Overall, don't recommend.

By James M

Apr 18, 2018

Autograder bugs make for a frustrating time completing the assignments. Independent research and self-guided learning will come in handy for this course as the lectures (mostly) are uninformative.

By Elias

Oct 17, 2017

A nice course overall but maybe not the best in the specilisation.

It may be me non understanding deeply the content but I found it a bit more mystical rather than quickly see concrete applications

By Muhammad H M

Dec 6, 2020

A reasonable course for a "first" look at natural language processing, but, you will definitely need complimentary resources for grasping NLTK concepts. Overall a reasonable introductory course.

By Robert S

Nov 15, 2020

Too much time required to review discussion threads to understand or fix problems with the coding assignments. Lectures provided little substantive assistance in handling the coding problems.

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.