Chevron Left
Вернуться к Анализ текстовой информации и аналитика

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

4.4
звезд
Оценки: 440
Рецензии: 106

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

JH

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.

DC

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.

Фильтр по:

76–100 из 105 отзывов о курсе Анализ текстовой информации и аналитика

автор: Watana P

Aug 23, 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.

автор: Aravindh

Apr 19, 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

автор: Ian W

Aug 10, 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.

автор: Darren

Aug 23, 2017

Hope the speaker can slow down sometimes.

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

автор: Hernan V

Sep 29, 2017

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

автор: Siwei Y

Mar 27, 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。

автор: Ryan L

Jul 27, 2018

Lots of great topics are covered. Would like to see more hands on exercises.

автор: Kim C

Jul 23, 2017

Full of intuitions about text mining. Hope I can absorb all those ideas soon

автор: Tanan K

Aug 12, 2017

Very complicated but useful for a deeper understanding of text mining

автор: Shaima M S

Jul 27, 2016

Very detailed, but taught in an easily understandable manner.

автор: Rahul M

Feb 08, 2018

ok ish course. Not highly recommended, but seems fine

автор: Norvin C

Oct 10, 2017

Generally quite clear explanations

автор: Amir Z

Sep 01, 2016

Good survey of techniques

автор: To P H

May 07, 2019

Very dense content

автор: Guillermo C F

Oct 16, 2017

Very good course!!

автор: Hyun J L

Nov 30, 2017

Was Quite Helpful

автор: Rahila T

Nov 15, 2018

Good

автор: Alexandr S

Jul 11, 2019

The Professor has a difficulty with English pronunciation, so sometimes it is very hard to understand his speech.

автор: Kaniska M

Sep 06, 2016

The coding assignment instructions are near impossible to follow. The lecture is monotonous in the later weeks.

автор: Gnaneshwar G

Feb 10, 2018

Its was alright. The author must try different approach or explain a bit more about the mathematical equations

автор: Ankur B

May 08, 2019

Little outdated but still clears the basics. More theoretical and less programming based

автор: Manvendra S

Sep 05, 2017

this course is useful if you take further courses too

автор: Quintus L

Nov 06, 2019

Great theoretical introduction, but not hands-on.

автор: Alexander S

Dec 16, 2019

Course was ok. Some slides have mistakes in it.

автор: Michael T

Sep 22, 2016

Forums were poorly organized and not well participated in.

There was no forum topic for the honors assignment.

Honors assignment appeared to require unix, which was not stated in the course requirements.

Honors assignment was due too early in the term.