미시건 대학교의 Applied Text Mining in Python 학습자 리뷰 및 피드백

I did not give 5 starts because Assignment 4 was really hard. It required too much knowledge from outside. The week 4 modules poorly prepared me for assignment 4.

Content-wise course is okey. But all the students faced issues during the assignment submissions because of the auto grader.

It is interesting, cognitive and very useful. But, there were very few answers from the teaching staff in the discussions at the forum. In previous courses of this and other specializations, the teaching staff took an active part in the forum and this greatly helped in understanding and fulfilling the tasks of the course.

Good intro course on NLP

Great course! very much handy if you are looking for a 'Text processing in Python' primer. The good thing about the course is that it explains the libraries. For example - NLTK vs SciPy for applying ML on text. What's missing, is the Deep Learning aspects of text processing

Good introduction into the field of text mining, but very brief. I think the structure could do with some fine tuning as for example the extraction of features from text is left mostly untouched or is covered by the home work only. All in all I found it slightly less well structured than the previous parts in the series, but it was still very useful and helpful as a starting point.

A good course which introduces you to the basics of text processing and text mining in python and exposes you to tools such as regex, nltk and gensim. While the lectures and assignments do promote this learning, a lot of the criticism that is directed at the course is due to the auto-grader issues. You can easily side-step a lot of these problems by going through the forums. However, I do think that the course could have been better planned and executed, even IF the only purpose is applied text mining for e.g., better context and some exposure to theory or at least pointers to where more material could be found for self-study would have been helpful. However, I did learn some things from the class giving me a push towards learning more on the subject on my own.

Great material with practical applications! I utilized a lot from this course in my work! I think the assignments should be made a little bit more clearer, specially the first one. Took a lot of time to do the first one, due to some exceptions that were not mentioned in the exercise, at least one should mention that there might be cases other than specified here.

Overall a great course! Thanks!

The lectures are in my opinion too concise. The programming assignments are very interesting. Perhaps the week 1 programming assignment is too complex.

I learned a lot about regular expressions, how to use NLTK to parse words and parts of speech, and to apply machine learning techniques from the third course to text.

The homework assignments were finicky with the autograder and often there was a lot of frustration regarding the exact data types of the output. I spent a lot of type debugging over simple things that could have been clarified in the assignment description. However, the discussion forums are active and people are willing to give feedback!

Nice introduction to the topic and interesting tools. The evaluation system could be improved adding more resources focused on the use of the nltk functions or giving some advice about the critical points in the Python demonstrations.

Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.

Great Content.

The class was great. However the assignments had a lot of problems.

Useful topic.

Overall this is well done course, but the autograder for week 2 needs a lot of work. It was buggy, broken and gave uninformative answers.

Awesome course..It is a good start for NLP. Comprehensively covers all topics. The Autograder for the programming assignment needs

overall a goods intro into text analysis

The course content is very interesting and high quality; however, the video slides include code that is not available in e.g. jupyter notebooks. Also, the assignment markers do not give any useful feedback - more than half of the time spent was usually when 99% of the task was complete but some very minor detail threw the marker off.

Learned lots of new stuff, but some details are not established well including autograder issue at the last assignment. Hope this gets cleared out soon.

Course material was good but week 4 assignment set up is a disaster.

Course is introductory and helpful. The staff should provide all the jupyter notebook code examples and provide better instruction for assignments.

Very good course. A little bit heavy on in-built functions which hides what is happening underneath, but overall very good.

Hard course, good concepts but needs more visualization of the concepts

Overall, a solid course, though it felt a bit like a face-to-face lecture course recorded to video. The material was helpful and well-explained, but I feel it could benefit from taking advantage of the MOOC medium more effectively, such as by providing code sample notebooks for the students to run and modify, which have been very helpful to me in understanding the material in other courses in the same specialization.