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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

4.3
3,455 個評分
662 條評論

課程概述

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....

熱門審閱

JR
2020年12月4日

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.

CC
2017年8月26日

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!

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

創建者 Mischa L

2018年1月3日

Good intro course on NLP

創建者 Eric G

2019年8月20日

The autograder sucks!

創建者 Ankit G

2018年11月18日

basic and nice course

創建者 Christian E

2019年3月27日

Very good content

創建者 bictor

2018年11月6日

Very interesting

創建者 Patrick L

2019年11月27日

It needs update

創建者 Liran Y

2018年4月7日

Great Content.

創建者 Yang F

2017年8月22日

Useful topic.

創建者 shubham z

2020年6月13日

good course

創建者 aditya r

2017年10月5日

Good Course

創建者 Chen G

2019年11月5日

Helpful..

創建者 Harsha V M V

2020年9月16日

Good one

創建者 Lalit S

2019年1月29日

Awesome

創建者 Sweta c

2020年8月20日

good

創建者 Rahila T

2018年11月15日

Good

創建者 Utkarsh T

2018年12月18日

NA

創建者 Alex F

2020年2月24日

p

創建者 Fedor K

2017年11月16日

G

創建者 Amit B h

2019年10月16日

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

2018年5月3日

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

2017年10月1日

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.

創建者 Ben E

2017年11月10日

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

2019年11月19日

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

2021年2月15日

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

2017年8月24日

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.