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Applied Text Mining in Python(으)로 돌아가기

미시건 대학교의 Applied Text Mining in Python 학습자 리뷰 및 피드백

3,188개의 평가
603개의 리뷰

강좌 소개

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....

최상위 리뷰


Aug 27, 2017

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!


May 04, 2019

Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)

필터링 기준:

Applied Text Mining in Python의 594개 리뷰 중 251~275

교육 기관: Palaparthi A

May 06, 2020

Very Good

교육 기관: David L

Dec 13, 2017


교육 기관: Reinaldo M

Jun 18, 2020


교육 기관: Mahmoud R

Jan 01, 2020


교육 기관: Su L

Mar 30, 2020

love it

교육 기관: sandeep d

Aug 03, 2019


교육 기관: Yoselin A

Aug 11, 2020


교육 기관: Yurii S

May 23, 2020


교육 기관: Yi-Yang L

Sep 04, 2017



Sep 17, 2020


교육 기관: Luis M R C

Feb 26, 2020


교육 기관: Muhammad M M

Jan 27, 2020


교육 기관: Yusheng F

Jan 06, 2020



Jul 25, 2020


교육 기관: Rifat R

Jun 21, 2020


교육 기관: Swapnajit R

Mar 11, 2020


교육 기관: Tianyang N

Aug 18, 2019


교육 기관: shantanu k

Jun 24, 2019


교육 기관: Parul S

Apr 20, 2019


교육 기관: Magdiel B d N A

May 12, 2019


교육 기관: Meixian W

May 10, 2019

The course material is good and I would give a 5-star for it. The reason why I took 1 star back is that the instructor seems to be not very well prepared for this course.

First, he used 'so' too frequently while lecturing. I am not saying that he should totally not use any filler words (like 'hmm' or 'um', and 'so' is one of them), but saying that using many fillers could cause distraction and confusion. As 'so' is one of the transition words, it implies a logical connection between 2 sentences. Using 'so' a lot was actually distracting me from following the course material because I had to identify which 'so' was a filler so that I could ignore it and which 'so' was a consequence indicator so that I could pay attention to the following sentence.

Second, he sometimes seemed to get lost with the slides. For example, from Week 3 Video "Learning Text Classifiers in Python" slide at 13:36, the slide was easy to understand by showing the codes saying "NLTK.classify has something called SklearnClassifier which could let you use some models from scikit-learn such as naive_bayes or svm and here are 2 examples", but his way of explaining the slide was quite confusing. This kind of "mistakes" cost me extra time to look at the scripts to make sure that I didn't misunderstand anything.

교육 기관: Gina G

Jun 16, 2020

Overall I think this is a great course. I learned a lot from it. The assignments for the first three weeks were great in quality, and even though I had to spend some time on some 'unnecessary debugging ' due to their Autotrader every time I submitted my assignments, it actually was not that difficult to figure out. So I think it's still worth it.

I gave four stars because I feel the final weeks' content was way too general. The videos in the fourth week only gave an overview of the subject from a very high level, provided no coding examples with real-life data. I feel there was a big gap between what was taught in the lectures and what was required in the assignment of that week. Also, the wording of the last assignment was very unclear.

I would recommend this course to others because the first three weeks' content was great and you could learn a ton from the first three weeks' assignments especially.

교육 기관: jie

Apr 30, 2020

I like week 1-3 of this course. week 4 is terrible though.

Week 1-3, Ilike this instruction and step by step assignment structure. I start to have some sense of NLP. However, week 4 is probably the week with shortest instruction. Very brief introduction to LDA etc, then a much much much more difficult assignment. It took me several days to read documentation and search stackflow to complete the assignment.

So, I finally know how to use regex in week1, start to know basic idea of tokenize and ps in week 2. and refreshed machine learning, actually, week3's ML instruction is better than course 3 of this specialization. Then week 4 is a hell. IF they really want to revise this course, I strongly suggest to have a clear case study to go through. This is a must for those who are not familiar with NLP.

교육 기관: Srinivas K R

Sep 16, 2017

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.

교육 기관: Dongliang Z

Jan 13, 2018

wk1-wk3 are good. w4 is a little weak to build the connection between texting mining and coding. Moreover, it will be more straightforward if the lecturer teaches more about the procedure to deal with text mining. I just passed this course but don't master text mining technique through it.

It is still a good introduction to texting mining, a very beginning of it.

My suggestion is that wk4 should be reconstructed to make people really believe they can use what they learn in this course after they pass the assignments.

Finally, thanks the lecturers for introduction. Especially thanks all students who contribute a lot in forum. Without them, I cannot pass the assignments.