Ratings and Reviews for Applied Text Mining in Python

broken assignments

Confusing explanations of NLP concepts. Inadequate explanations of how to use the Python packages to solve the assignment questions. I'm writing this review half-way through the Applied Social Networks Analysis course which is excellent and pitched just right. The contrast between the 2 courses couldn't be greater.

Helpful..

A little bit stretched my python skill, but learned a lot. Forum is a good place, and maybe next I will join some study group online or offline to have more discussions.

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

Really good course... The teacher is great

The course wasn't totally bad but it definitely wasn't as good as the first three. I felt I was thrown in with insufficient tools to cope with the assignments. Relying on the internet is important but in these cases, you have to rely on it quite heavily. On assignments 1 and 3 in particular, Upon final submitting, I felt I didn't learn much at all. Specifically with regexs, I feel extremely insecure with my regex skills and that is an understatement. I don't think that is something that should happen after a text mining course. The following remark *isn't* a crucial one: For a non-native English speaker understanding the language could sometimes pose an obstacle. Now, decoding the lecturer's accent is yet another obstacle on top of the former. Lecturer with an American accent will obviously be the best choice.

I have taken and passed all the first four courses in this specialization, and very much liked the first three courses. But the quality of this course on text mining is far below the average level of the first three. Go find some other courses if you want to learn text mining with Python. There are too many areas of flaws in this course. I am only highlighting the top 5 below: 1. lacks good connection throughout the course content. This problem exists almost everywhere, both from slide to slide within a video and from video to video. Many times you would have questions in your head like “why is he talking about this?” or “what is this?” 2. use example just for the purpose of showing examples. Don’t really explain the point it is supposed to explain. In many times the examples do not provide clarity, but raise more confusion instead. 3. assignment tasks either too simple, or remotely related to what is introduced in the course. The worst case is assignment in week 4, where the assignment is so poorly constructed. You have to spent days to figure out the right answer. They call it “debug”, but there is nothing wrong with my code. I would say it is more of a process to “try to figure out what the instructor is asking for”. 4. talks too much about the theoretical things, not very good introduction of using python. Even when python code is demonstrated, it is almost always in a very abstract way. This is significantly different from the first three courses, and very annoying. You would need to spend about the same amount of time googling how the packages work as I have never took the course. 5. Repetition of content already introduced in previous courses, i.e., machine learning basics.

Well-structured, awesome pedagogy and challenging assignments. It has all the elements it takes to make a MOOC epic! Thanks UMich and Coursera for helping put this course together and allowing me to pursue it.

totally can't understand the Indian accent.