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

3,455 個評分
662 條評論


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....



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.


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!


426 - Applied Text Mining in Python 的 450 個評論(共 653 個)

創建者 Mischa L


Good intro course on NLP

創建者 Eric G


The autograder sucks!

創建者 Ankit G


basic and nice course

創建者 Christian E


Very good content

創建者 bictor


Very interesting

創建者 Patrick L


It needs update

創建者 Liran Y


Great Content.

創建者 Yang F


Useful topic.

創建者 shubham z


good course

創建者 aditya r


Good Course

創建者 Chen G



創建者 Harsha V M V


Good one

創建者 Lalit S



創建者 Sweta c



創建者 Rahila T



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創建者 Amit B h


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.

創建者 Steve M


The content of this course has great potential, but needs significant refinement. The lectures, while delivered with enthusiasm, were very theoretical/academic and provided little in the way of preparation for the more practical exercises. The disconnect between lectures and assignments, coupled with technical challenges (autograder glitches) were frustrating. The only support came from one dedicated volunteer Coursera Mentor; the instructor cadre was absent or unavailable to students throughout the four week period. The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.

創建者 Samuel E


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))


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.

創建者 Ben E


This course did cover some good topics (Naive Bayes model, similarity, part of speech tagging). However, I felt the homework was more about manipulating Python data structures than learning anything significant about text mining. Some of the theory behind the models was covered, but didn't make it to the homework.

It would be difficult since this is a short class, but I would have preferred more about tips on which model to use and feature engineering / selection, and examples of practical applications of text mining. (Or stories of failures in the instructors' experience!)

創建者 Wenlei Y


This course compared with the others in this specialization, is not-as-well organized. You might have to spend lots of time working on the assignments by yourself (i.e. you cannot find related guidance in the course materials); There is less helpful online information, compared to course 1-3 in this specialization, either - so it is a little painful to do these assignments. However, the tools and the theories behind them are useful and powerful. If you are really interested in text mining, you will benefit a lot! The instructor is passionate and humorous.

創建者 José G G


In my opinion this course lacks of clear goals, it is not easy to understand where the instructor is going and also the autograder was really confusing. I spent hours struggling with it and from the opinions in the forum it is a common issue. On top of that, several homeworks were 'recipe' oriented, I mean you have to follow a procedure without actually understanding the concepts behind it. From the positive side, this was my first sight to this topic and in a general manner I got a sense of what it is about, but it was very superficial.

創建者 Jim B


Of all of the Applied Data Science with Python classes I have taken, this was the worst. If it were not for the discussion groups I would not have been able to complete the course. And the discussions groups requested help from instructors and received little to none. Part of the problem is that the auto-graders were broken, the rest of the problem was that this class relied on the online documentation. And of the classes in Applied Data Science with Python, this one has the worst documentation. Hence the class needed more help.