Ratings and Reviews for Applied Text Mining in Python

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

The grading system is supremely messed up and at least I have a vague idea what am talking about because I have completed more than a dozen coursera courses. Also, the methods used through the courses teaches very bad coding approach relying on global variables. Below is an example from Module 2: def example_two(): return len(set(nltk.word_tokenize(moby_raw))) # or alternatively len(set(text1)) example_two() Why would they not pass moby_raw and text1 as arguments in the function? With that said, the course could easily be one of the best intro NLTK courses out there minus the frustration and very poor design.

very good course that taught me a lot!

One of the worst courses I have ever attended. The subject is treated on the surface. The exercises are sometimes not covered in the lectures. The auto-grader is badly configured. It was annoying and frustrating to do the exercises. Sometimes an untold oderering of the results was expected. Sometimes an untold normalization has to be done.

Interesting topic, but a really poor course with barely any content. Around an hour or less of lectures a week. I've taken a lot of MOOC's on Coursera and other platforms and this one is poor

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.

Excellent course! Video lectures are high quality, with realistic problems and applications. Exercises are reasonably challenging, and all quite fun to do! Strongly recommend this course

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.

While the course gives a good broad understanding of how any NLP task would work in theory, but the course is very unstructured. For example, if I had to be a given task on doing a sentiment analysis, I can broadly tell what is the conceptual theory behind it but I dont know how exactly to do it because the professor talked about so many tools which were repetitive in their use and were not clearly demarcated as to what tool should be used for what?

autograder 经常 犯些低级错误， 导致很多人在对付 autograder 上 花了很多时间。 请授课方务必改正， 否则 有不负责任之嫌。另外 编程作业的 说明委实不清不楚， 模棱两可。除此之外内容还算中规中矩， 虽然我个人 认为太表浅了一些。 Autograder is so buggy, that people have to spend lots of time to figure out, what the solution is. Additionally, the Instuction of python assignment is often ambiguous. Please fix them ASAP. Personally I find that the content is somehow like an introduction. I had hoped something more about detail.