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返回到 文本挖掘和分析

學生對 伊利诺伊大学香槟分校 提供的 文本挖掘和分析 的評價和反饋

4.5
598 個評分
133 條評論

課程概述

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....

熱門審閱

JH
2017年2月9日

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
2018年3月24日

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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101 - 文本挖掘和分析 的 125 個評論(共 132 個)

創建者 Ryan L

2018年7月27日

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

創建者 Shubhra V

2020年7月21日

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

創建者 Kim C

2017年7月23日

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

創建者 Tanan K

2017年8月12日

Very complicated but useful for a deeper understanding of text mining

創建者 Jan-Henk P

2020年6月6日

More examples/questions during the course in using the formulas

創建者 Shaima M S

2016年7月27日

Very detailed, but taught in an easily understandable manner.

創建者 Rahul M

2018年2月7日

ok ish course. Not highly recommended, but seems fine

創建者 Rohit C

2020年4月8日

Text Material is good and much more informative.

創建者 Norvin C

2017年10月10日

Generally quite clear explanations

創建者 Amir Z

2016年9月1日

Good survey of techniques

創建者 Savindu V K

2020年7月27日

Really good course.

創建者 To P H

2019年5月6日

Very dense content

創建者 Guillermo C F

2017年10月16日

Very good course!!

創建者 Hyun J L

2017年11月29日

Was Quite Helpful

創建者 Rahila T

2018年11月15日

Good

創建者 Martin B

2020年9月26日

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.

創建者 Alexandr S

2019年7月11日

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

創建者 Kaniska M

2016年9月5日

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

創建者 Gnaneshwar G

2018年2月10日

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

創建者 Tali L

2020年3月22日

Awesome content. However, the lectures were slow and many were longer than I thought they needed to be.

創建者 Ankur B

2019年5月8日

Little outdated but still clears the basics.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

創建者 Manav

2017年9月5日

this course is useful if you take further courses too

創建者 Quintus L

2019年11月6日

Great theoretical introduction, but not hands-on.

創建者 Alexander S

2019年12月16日

Course was ok. Some slides have mistakes in it.

創建者 Leonardo P

2020年6月25日

Hot topic but a obsolete material.