ChengXiang Zhai
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. The University of Illinois at Urbana–Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
Instructor Details
Courses : 2
Specification: Text Mining and Analytics
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49 reviews for Text Mining and Analytics
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 19 hours |
| Year | 2016 |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
Darren –
Hope the speaker can slow down sometimes. It will be more helpful if give more real world examples
Kyle A –
I have had a pleasure in taking this course, with the heavy focus on theory and applications of text mining. This course provides a comprehensive overview of text mining and analytics, which is incredibly useful for academic works and career alike.
Manvendra S –
this course is useful if you take further courses too
geoffrey a –
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.
Ch N –
Good course, but if combined with weekly assignments in python and R it would be even better than any other course.
Eugenio L C –
While interesting, the videos are too long, and few practical
Hernan V –
Excellent course, but not a deep coverage of more complex text analysis algorithms
Hossein A –
One of the best courses I took in Coursera. Well managed, well presented, valuable information is provided.
Norvin C –
Generally quite clear explanations
Guillermo C F –
Very good course!!
George P –
Outstanding mix of theory and practical applications to help understand the theory. Well organized and excellent presentations. Thank you!
Hyun J L –
Was Quite Helpful
Matt R C –
Excellent course, have to really listen to the instructor but course information is excellent!
Michael H –
Good course, more practical examples of the different techniques would be helpful!
Cheng–shuo Y –
It is a great course!
Isaiah M –
T
GANG L –
The content layout of this course is very good. It gives a big picture about text mining. It’s very hard to explain text mining just in 6 weeks course. But Professor did it. I hope professor can provide further class of text mining, provide more detail case to explain some algorithm not covering in this class and cover more new topic like knowledge graph etc.
Julie W –
Useful course to build foundation for text mining and NLP especially for beginners.
John –
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!
Rahul M –
ok ish course. Not highly recommended, but seems fine
Gnaneshwar G –
Its was alright. The author must try different approach or explain a bit more about the mathematical equations
SAYANTAN D –
Womderful course material with lucid videos and interesting quiz patterns.
David C –
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.
Paul N –
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
David O –
Great course
Scott C –
Presentation has a lot of room for improvement to present the information where other people can comprehend the topic.
Ryan L –
Lots of great topics are covered. Would like to see more hands on exercises.
Uday A K –
i have completed the course but didnt receive certificate and there is no option for id verification
Ian W –
In depth description on the algorithms. Personally I suggest finish the quiz of the nth week after finishing all the video of (n+1)th week.
Luis F Y B –
Great..Clear. Thanks
Gourav A –
Excellent course.
Rahila T –
Good
Yugandhar D –
Excellent course the provides comprehensive knowledge on Text Mining and Ananlytics in all its dimensions.
Yaoyao D –
It is rare to find an online course that explains the statistics and intuition behind text mining and machine learning algorithm!
YASH L –
The course was very challenging and i learn a lot of new things from the course, this will help to complete my project.
To P H –
Very dense content
Ankur B –
Little outdated but still clears the basics. More theoretical and less programming based
Mrinal G –
Nice
Ruben D S P –
great clases about text mining, this is really important becuase many people required this type of data analysis all of the time
shaoming.xu –
Excellent courses. Prof Zhai provide a lot of insight in this course!
Shubham K –
Couldnt understand a word of what the instructor said
Alexandr S –
The Professor has a difficulty with English pronunciation, so sometimes it is very hard to understand his speech.
Amar J –
This course teaches you the very nitty gritty details of text mining. It has been an enriching experience for me in this course.
Nazar K –
Initially it was very complex and seemed very theoretical but it all comes together amazingly at the very end.
Hai Q P –
The course helps me delve deeper into my research. Very helpful for researchers in graduate schools!
MItrajyoti K –
Very good
Quintus L –
Great theoretical introduction, but not hands on.
Alexander S –
Course was ok. Some slides have mistakes in it.
Ahmed M S –
This is an excellent foundational course about text mining. It provides a very solid theoretical foundations and concepts about the subject. The only thing that felt missing, is giving more numerical examples during the video sessions to ease understanding the formulas.