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Avis et commentaires pour d'étudiants pour Exploitation de text et analytique par Université de l'Illinois à Urbana-Champaign

602 évaluations
133 avis

À propos du cours

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....

Meilleurs avis

9 févr. 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.

24 mars 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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76 - 100 sur 132 Avis pour Exploitation de text et analytique

par Kumar B P

8 mai 2020


par R M

29 avr. 2020


par MItrajyoti K

24 oct. 2019

Very good

par Hernan C V

4 mai 2017


par Arefeh Y

4 nov. 2016


par Swapna.C

17 juil. 2020


par Mrinal G

20 mai 2019


par Isaiah M

2 janv. 2018


par Valerie P

11 juil. 2017


par Deepak S

11 août 2016


par Jennifer K

5 juil. 2017

Despite the amount of material to cover, this course did a great job of introducing the right amount of detail for various aspects (motivation, algorithms, algorithmic reasoning, evaluation) on topic modelling, text clustering, text categorization, sentiment analysis, aspect sentiment analysis, evaluation of text and non-text data in context, and more. Definitely read the additional resources for the material - it will give you an incredibly in-depth view to what you learned in the lectures and also give you a start on implementing the covered algorithms on your own.

The only thing I missed in this class are assignments for implementing the algorithms in a language other than C++ and in a framework other than MeTA. It would make sense to provide this opportunity in additional, commonly-used data-science languages such as Python!

par Milan M

14 sept. 2016

This is an excellent course that captures many different text mining techniques. It requires some math knowledge in numerical analysis and probability in order to understand the concepts.

I gave 4 star rating due to 2 problems during the course:

1) Lack of examples along the formulas and principles. There are some, but many concepts could be adopted much faster if examples were introduced right along with them.

2) The optional programming exercises are easy to complete, but the environment is very confusing to set it up.

par Gonzalo d l T A

10 mai 2017

A really interesting course which covers theoretically most of the text mining techniques. I missed having more practical exercise, which could help to deeply understand the lectures. Setting up the environment for the development task is a little bit complicated, it might be interesting to provide a virtual machine with all the software and correct versions required. Even though, I would recommend this course if you are interested on the topic.

par Arkadiusz R

9 juil. 2017

Very good course with a lot of essential information about problems correlated with text understanding. It give me general look for text mining topic. Some lectures give only overall information about text analysis problem, but it still gives me an opportunity to learn about these listed topics to resolve relevant problems. I recommend this course anyone!

par fakhriabbas

2 oct. 2016

the course is very helpful in giving the overall flavor of text mining and analytics. I would recommend to reduce the number of math work and focus on the conceptual level along with more application that could be used. For the math part, adding optional videos for more details about math will be very useful and helpful

par Ahmed M S

12 janv. 2020

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.

par Alex D T

22 juil. 2017

Professor Cheng has a deep knowledge of the subject and presents a diverse topic in a very condensed set of courses. Material is well presented, but some of the quizzes and slides need to be better organized.

par Akarapat C

11 févr. 2017

This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.

par Watana P

22 août 2017

Most of the lessons are mathematical formulae in which, in my opinion, I need more real case study/practice to make myself clearly understand on how do those formulae perform.

par Aravindh

19 avr. 2017

The content is really good but the course has too much theory. Mixing it with some practical programming assignments would have been very nice

par Ian W

10 août 2018

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.

par Cihan T

5 nov. 2020

Nice course for the people who want to acquire knowledge about mostly the theoretical part of certain NLP methods.

par Darren

23 août 2017

Hope the speaker can slow down sometimes.

It will be more helpful if give more real-world examples

par Hernan V

29 sept. 2017

Excellent course, but not a deep coverage of more complex text analysis algorithms

par Siwei Y

27 mars 2017

老师选择的课题非常丰富 , 讲解的逻辑脉络也非常清晰, 这是许多所谓的大牛教授所无法做到的 。

只是不知道为何, 论坛太过冷清, 里面似乎也没什么 人负责解答问题。