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Applied Text Mining in Python(으)로 돌아가기

미시건 대학교의 Applied Text Mining in Python 학습자 리뷰 및 피드백

2,050개의 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


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 382개 리뷰 중 251~275

교육 기관: Vishal S

Jul 16, 2018

Lectures are good but the assignment of week1 and week 4 is a little bit absurd and unclarified. Autograder is too slow.

교육 기관: Imran A G

Sep 24, 2018

Good for basic understanding only

교육 기관: Eric S

Sep 27, 2018

Most assigmets were not in the notes. Still everyhting seems really usefull.

교육 기관: Raivis J

Aug 11, 2018

Graded assignments need more grounding in practically applicable situations.

교육 기관: 陆恩哲

Aug 30, 2018

I don't think the lecture is very clear. Although I have finished the homework, I still feel a little confused about the concepts.

교육 기관: Vasilis S

Sep 01, 2018

Poor ability from lecturer to explain key concepts.

교육 기관: Jeffrey D B

Oct 16, 2018

The course would be significantly improved if there were more hands-on demos during the lectures. Lectures are very high-level and aren't terribly useful when trying to do the lab exercises.

교육 기관: George M J

Nov 15, 2018

Good content.

Had to spend way too much time fighting the auto-grader.

교육 기관: pavan b

Nov 19, 2018

good training

교육 기관: Craig A B

Nov 19, 2018

You do more work learning on your own to be able to do the projects and quizes then is given in the lectures. These University of Michigan classes aren't very balanced in terms of lectures, reading, and difficulty of projects.

교육 기관: Ben E

Nov 10, 2017

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!)

교육 기관: Vijay C S

Oct 24, 2017

The course is pitched at a introductory level. I would have like to have more practical tutorials.

교육 기관: Siddharth S

Jun 12, 2018

The fact that the strategy of a Jupyter Notebook Demonstration during explanation was not followed in week 3 and 4 was a disappointment.This specialisation had been wonderful with its use of demonstration in Lectures with the Notebook,If this had been followed in Week 3 and Week 4 then the course would definitely had shined.Please correct the same, the course deserves that, It has wonderful content.

교육 기관: Paula C R

Aug 04, 2017

I think the course was superficial and could be better explored. It's good start, though.

교육 기관: Brian R v K

Oct 30, 2017

I enjoyed this course, but some aspects of it felt "light touch", particularly week 4. That week would be greatly improved with a jupyter notebook and an applied demonstration by the absolutely awesome Teaching Assistant, Filip Jankovic. Whenever he does a demonstration, it's clear, concise, practical, and always helpful. Let's see more of him!

교육 기관: Jonathan B

Aug 10, 2017

While the video of the course were OK, the assignments were of really bad quality. So many problems with the auto-grader, and some questions were absolutely not clear. I still put 3 stars because the subject is interesting and I got things I can work with out of it, but don't expect too much from it, you'll spend most of your time trying to deal with the weird assignments questions. For the time spent, they could have added 1 or 2 weeks of videos.

교육 기관: Rasoul N

Dec 26, 2017

Course materials are amazing but there are not much support for assignments.

I did all the quizzes and assignments except the last one. It seems there was something wrong with auto-grader or the assignment was not clear. There were complains about this issue on the forum but no one from staff answered the questions.

교육 기관: Georgios P

Oct 30, 2017

Week 4 was not sufficient

교육 기관: samuel e

Oct 01, 2017

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.

교육 기관: Panit A

Oct 22, 2017

Bad assignment. Grader not reliable. No control over the discussion board, many confusing comments mixed with good comments.

교육 기관: Nitish K

Sep 16, 2017

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?

교육 기관: Bernardo A

Aug 19, 2017

I liked the course, but it felt as a very raw overview, I think it could have been more challenging when it comes to the models explained.

교육 기관: Juan C E

Oct 30, 2017

A bit lack of coherence in theory. Sometimes, the theory needed for the assignments was not given with enough detail, and you had to browse the forums for the information, and applying it to your assignment just to pass, sometimes without understanding why you were doing what you were doing.

More Python examples needed. For week 3, the tutorial about recommender systems was perfect for the assignment.

교육 기관: Joan P

Nov 07, 2017

A lot of issues with the auto graders

교육 기관: Ling G

Sep 01, 2017

I like the lecture very much. If the lecture can cover more example codes it iwll be greater.