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Explore algorithms for mining and analyzing big text data to discover interesting patterns, extract useful knowledge, and support decision making.
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.
This course will be covering the following topics:
Overview of text analytics and applications
Extending a search engine to support text analytics (text categorization, text clustering, text summarization)
Topic mining and analysis with statistical topic models
Opinion mining and summarization
Integrative analysis of text and structured data
Proficiency in programming with either C++ or Java. Basic knowledge of probability and statistics.
The course will have video lectures, accompanied by quizzes and peer graded assignments.
How does this course fit into the Data Mining Specialization?
This is the fourth course in the track.