Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.
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University of Illinois at Urbana-Champaign
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.
- 5 stars65,45 %
- 4 stars23,98 %
- 3 stars6,82 %
- 2 stars1,65 %
- 1 star2,09 %
Top-Bewertungen von TEXT RETRIEVAL AND SEARCH ENGINES
Great introductory course. It has opened my eyes to new challenges that face information retrieval. DON"T TAKE THIS COURSE IF YOU WANT TO LEARN elasticsearch.
Course was well taught the instructor's explanation of the topics was very comprehensive. Overall satisfied with the experience
This course goes through the basics of text retrieval systems with an appropriate speed. However, the contents are quite out-dated for 2019.
A bit difficult to complete as the Quiz questions were tougher. But when you go through all, you might feel good.
Über den Spezialisierung Data-Mining
The Data Mining Specialization teaches data mining techniques for both structured data which conform to a clearly defined schema, and unstructured data which exist in the form of natural language text. Specific course topics include pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. The Capstone project task is to solve real-world data mining challenges using a restaurant review data set from Yelp.
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