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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: 668
Рецензии: 141

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

9 февр. 2017 г.

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 мар. 2018 г.

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:

101–125 из 140 отзывов о курсе Анализ текстовой информации и аналитика

автор: Watana P

22 авг. 2017 г.

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

19 апр. 2017 г.

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

10 авг. 2018 г.

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: Cihan T

5 нояб. 2020 г.

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

автор: Darren

23 авг. 2017 г.

Hope the speaker can slow down sometimes.

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

автор: Hernan V

29 сент. 2017 г.

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

автор: Siwei Y

27 мар. 2017 г.

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

27 июля 2018 г.

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

автор: Shubhra V

21 июля 2020 г.

Very detailed course. Helps in gaining complete understanding of text mining

автор: Kim C

23 июля 2017 г.

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

автор: Tanan K

12 авг. 2017 г.

Very complicated but useful for a deeper understanding of text mining

автор: Jan-Henk P

6 июня 2020 г.

More examples/questions during the course in using the formulas

автор: Shaima S

27 июля 2016 г.

Very detailed, but taught in an easily understandable manner.

автор: Rahul M

7 февр. 2018 г.

ok ish course. Not highly recommended, but seems fine

автор: Rohit C

8 апр. 2020 г.

Text Material is good and much more informative.

автор: Norvin C

10 окт. 2017 г.

Generally quite clear explanations

автор: Amir Z

1 сент. 2016 г.

Good survey of techniques

автор: Savindu V K

27 июля 2020 г.

Really good course.

автор: To P H

6 мая 2019 г.

Very dense content

автор: Guillermo C F

16 окт. 2017 г.

Very good course!!

автор: Hyun J L

29 нояб. 2017 г.

Was Quite Helpful

автор: PRANAV N

18 мар. 2021 г.

great course

автор: Rahila T

15 нояб. 2018 г.


автор: Martin B

26 сент. 2020 г.

This course is a mixed bag. The instructor is precise and to the point. It covers quite a few techniques that are usually not covered in other machine learning courses and offers good suggestions for additional reading to get into specific technical details. There are however two main drawbacks. First: there is only a single optional programming assigment in C++. Learning materials like these is often more thorough with programming assignments attached to them, which is the case in all of the best courses in the field of Machine Learning or Data Science. Second: the instructor's English is not great. This makes the course difficult to follow sometimes, especially since the automatically generated subtitles tend to be VERY bad and occasionally misleading.

автор: Alexandr S

11 июля 2019 г.

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