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Вернуться к Анализ текстовой информации и аналитика

Отзывы учащихся о курсе Анализ текстовой информации и аналитика от партнера Иллинойсский университет в Урбане-Шампейне

Оценки: 431
Рецензии: 104

О курсе

This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications....

Лучшие рецензии


Feb 10, 2017

Excellent course, the pipeline they propose to help you understand text mining is quite helpful. It has an important introduction to the most key concepts and techniques for text mining and analytics.


Mar 25, 2018

The content of Text Mining and Analytics is very comprehensive and deep. More practise about how formula works would be better. Quiz could be not tough to be completed after attending every lectures.

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1–25 из 103 отзывов о курсе Анализ текстовой информации и аналитика

автор: David C B

Feb 10, 2017

Annoyingly, the specific pre-requisites can't be determined until AFTER enrolling. This course is in C++, so if you're not a C++ programmer, it's probably not a good idea to purchase this.

автор: Essam D

Aug 26, 2016

The course focuses more on the theoretical side with no practical examples. It also does not explain these theoretical concepts in detail enough. It was difficult for me as a new learner in the text analytics field to follow such dense theoretical concepts.

автор: Paul N

Jun 08, 2018

I got the sense that this course needs to be updated. Some of the quizzes covered work that had not been treated up to that point and the programming assignment did not work with the most recent version of the MeTA toolkit. For a course of this stature I would have expected a lot more attention to such detail. It would also appear as though the owners of the course material are not present on the forums with students left to their own devices.This course as well as the Text Retrieval one does not compare well with the Machine Learning course from Stanford offered on Coursera when considering the above issues.Some work is required I believe

автор: 象道

Dec 31, 2016

This course is a pretty good resource for full-time graduates who are doing research in Natural Language Processing, but not for other people who want to learn in part-time some concrete skills to resolve specific text mining problems.

The course-style is heuristic-guiding based. Some questions are presented in lecturing or quiz, and answers are hardly found. So, some text books had better be specified.

Homework of this course is quiz-based, only one optional programming task. It's easy to pass the course, but it's hard to master its content. As an application science area, more practical assignments should be provided, as can help students learn better.

This is a 6-week open course teaching a complicated research area, Natural Language Processing, so most of its topic cannot be discussed in detail. However, this course could be designed and organized better.

автор: Sawal M

Apr 18, 2017

I think the course has very limited practical problems; so for beginners in NLP and text analytics, it is very difficult to grasp all the theoretical concepts presented in the course.

автор: Gary C

Jul 24, 2017

The content of the course is quite good. Professor Cheng explains the concepts well and I did learn quite a bit. However I feel that these courses have been all but abandoned by any support personnel. So far this is not a series I would recommend for anything but watching video lectures.

автор: John

Feb 07, 2018

The topics were interesting and Cheng was very motivated.

But often I found I didn't understand why we were spending a lot of time with the explanation of one thing and very little on another one. E.g., Bayes theorem was intensively discussed when Cheng explained the Naive Bayes Model, but the theorem had been heavily used prior to that. The concept of "Mixture Model" was asked about several times in tests, but I couldn't remember whether it had been defined. K-NN was subject of the exam before it had been handled in the course. I found the explanation of CPLSA wasn't enough for understanding its mechanics. Personally, I would have liked to understand LDA better.

Notwithstanding, thank you for this course!

автор: Shreya P

Jul 04, 2017

I didnt feel like continuing, I had problem at job for which i enrolled in course,to do efficient text mining..Its all theoretical..Too much information at one short and no examples relating to it

автор: geoffrey a

Sep 06, 2017

This is a great course for data science. I hope to use many of the techniques that were explained. There is plenty of cutting edge material here. It is essential for modern data science practice in my opinion. It's fairly advanced level. Students of this course will do just fine though, if they already have the ability to pass university level undergraduate computer science courses.

It would be a better course if the MOOC students received more attention from the teaching staff. The participation rate in the forums by students as well as staff was pretty low. As such it requires a strong and independent student to pass this course. It would also be a better course if there were more coding homeworks using something like jupyter notebooks. Also I would make the course a few more weeks long to handle the extra homework which I am suggesting.

This is probably the best MOOC course on this material in existence, to my knowledge. Highly recommend this course for anyone who intends to be a data science practitioner.

автор: Samir A G

Feb 25, 2017

Very good course thank you

I wish we could have use case applications with high level tools such as R

Thanks a lot again !

автор: Scott C

Jul 06, 2018

Presentation has a lot of room for improvement to present the information where other people can comprehend the topic.

автор: Lee X A

Jul 18, 2016

You need to pay to participate in the quizzes. Stay clear, there are free alternatives out there

автор: Yugandhar D

Jan 02, 2019

Excellent course the provides comprehensive knowledge on Text Mining and Ananlytics in all its dimensions.

автор: Yaoyao D

Feb 12, 2019

It is rare to find an online course that explains the statistics and intuition behind text mining and machine learning algorithm!

автор: YASH L

Apr 11, 2019

The course was very challenging and i learn a lot of new things from the course, this will help to complete my project.

автор: Viacheslav D

Dec 01, 2016

Best NLP course that I saw.

автор: Hongzhi Y

Aug 01, 2016

Very practical. The lecture is easy to follow.

автор: Godwin I

Aug 27, 2016

Excellent Module!

A most know & understand unit for all students of Data Science. Enjoyed every aspect of the learning .. Good teaching !!!

автор: Arefeh Y

Nov 05, 2016


автор: Mohan R

Jul 23, 2017

The workflow is clear and the professor speaks to the students directly about all aspects without skimming the material.

автор: Hossein A

Oct 05, 2017

One of the best courses I took in Coursera. Well managed, well presented, valuable information is provided.

автор: Julie W

Jan 22, 2018

Useful course to build foundation for text mining and NLP especially for beginners.

автор: Julien P

Jul 06, 2017

As a former compute science undergrad who wanted to get more knowledge into ML and NLP, I found this course to be both a very nice introduction and a progressive dive into more recent and advanced techniques. The structure suited my needs very well and was easy to follow along.

автор: Rose C

Jun 25, 2017

very theoretical. very insightful too. finally got a glimpse how the features that I took for granted took shape ... thanks to the instructor, I love the textbook as well ~

автор: Shin, Y

Nov 15, 2016

loved all the lectures