學生對 密歇根大学 提供的 Applied Text Mining in Python 的評價和反饋

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.

course materials are minimal and possible insufficient to complete assignments without additional reading materials. Assignment questions can be clearer with sample output will be very helpful.

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!

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!

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.

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.

There were significant issues with the autograder and the instructions for the programming assignments. This course has been around for a while. Why aren't they fixed???

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..

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.

The assignments were far more beyond the content covered, hopefully either the content covered to be extended or the assignment scope to be changed.

A lot of prior knowledge or independent learning is required to get the most out of this course. Needs more code walkthroughs.

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

Instructions for assignments are vague and incorrect. Instructor was hard to follow during lecture.

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

poor organization of the lesson and many many mistakes during assignments

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

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

it was so basic ! i was expecting some more detailed course on nlp

Very basic, but clear - just lacks the depth i was expecting

I found the course quite dry and hated the auto grader...

Far from expectation, feel upset

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.

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.

Compared to other courses in the Applied Machine Learning focus, this is so far the worst. The content and quality are poor. The lecturer is too slow and fails to prepare the student for the assignments. First week is very basic and ends with an assignment in regex. There's plenty of regex resources out there. 2nd week moves forward but finalizes in an assignment that involves concepts not covered in the lecture (ngrams). Weeks 3 and 4 contain too many errors in the lecture and autograder (use of AUC, finding minimum of a sparse array). UofM should rebuild this course.