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Applied Text Mining in Python(으)로 돌아가기

미시건 대학교의 Applied Text Mining in Python 학습자 리뷰 및 피드백

4.2
2,043개의 평가
389개의 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

CC

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!

GK

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 381개 리뷰 중 201~225

교육 기관: Vinicius G

Dec 26, 2017

I did not give 5 starts because Assignment 4 was really hard. It required too much knowledge from outside. The week 4 modules poorly prepared me for assignment 4.

교육 기관: Christos G

Aug 22, 2017

This was a very well thought and assembled set of Text Mining applications in Python. The complexity and profoundness of the topic somehow prohibited the instructors from sufficiently explaining the details in some occasions, which might eventually cause frustration with the students. However, perhaps this wide-first approach versus the deep dive is preferable for the purpose of the course. In all cases, Google and Stackoverflow will always remain as last resorts and supporting information sources.

교육 기관: Чижов В Б

Dec 18, 2017

It is interesting, cognitive and very useful. But, there were very few answers from the teaching staff in the discussions at the forum. In previous courses of this and other specializations, the teaching staff took an active part in the forum and this greatly helped in understanding and fulfilling the tasks of the course.

교육 기관: Saber D

Jan 17, 2018

Very useful course to text mining.

교육 기관: Jeremy L

Oct 26, 2017

The course itself is good, but the assigment system is not robust and some sentences are also ambiguous to users. Seeing from the forums, many users get confused in the assigments.

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.

교육 기관: Aditya R

Oct 05, 2017

Good Course

교육 기관: Petras V

Nov 24, 2017

Very good course. A little bit heavy on in-built functions which hides what is happening underneath, but overall very good.

교육 기관: Jesús P S

Jan 17, 2018

Hard course, good concepts but needs more visualization of the concepts

교육 기관: Zihao H

Mar 18, 2018

The final assignment is not well worded, and answer for the autograder is too strict.

교육 기관: Dinesh D

Dec 15, 2017

Course material was good but week 4 assignment set up is a disaster.

교육 기관: Fang F

Aug 29, 2017

Course is introductory and helpful. The staff should provide all the jupyter notebook code examples and provide better instruction for assignments.

교육 기관: Gunjari B

Jun 18, 2018

Lecture materials are not comprehensive enough to solve the assignments. Course is dependent on precursor courses in the specialization. Assignments often require reference from upcoming weeks. lectures are inadequate. The course is average at its best

교육 기관: lohith p

Aug 22, 2018

Good Material for the people who wants to start NLP. Thanks a lot for the material

교육 기관: Rajat B

Aug 24, 2018

The frequent and well thought out exercises are very helpful

교육 기관: Ivan S F

May 03, 2019

Great course. Worth taking it. Hard, but you will learn a lot. Homework 1 is confusing and discouraging, but pass it and the rest gets more interesting.

교육 기관: Henri

Apr 19, 2019

Great course, but expect to spend a lot of time on the assignments because of errors/bugs in the questions/autograder.

교육 기관: Rajendra S

May 09, 2019

Good course. But, I was expecting more depth.

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: CHITRESH K

Apr 29, 2019

Nice introductory course to NLP , give an insight into the topic .

교육 기관: Light0617

May 15, 2019

the assignment is so strange...

교육 기관: Samuel O

May 16, 2019

Nice, but first assignment shouldn't be considered here I think

교육 기관: Thúllio D M Z

Jun 17, 2019

The module 4 could have more hours. The key concepts are passed too fast and doesn't have a notebook with the classes content.

교육 기관: Harshith S

Jun 19, 2019

What an improvement from the previous few courses. The instructor teaches much better. I could mine text in my sleep now

교육 기관: Shashidhar s

Jun 28, 2019

Ultimate course for any one to start with on Data Science using Python.