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.
This course is part of the Data Mining Specialization
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Data Mining SpecializationUniversity of Illinois at Urbana-ChampaignAbout this Course
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Course 3 of 6 in the
Approx. 33 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
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Try Coursera for BusinessSkills you will gain
- Data Clustering Algorithms
- Text Mining
- Probabilistic Models
- Sentiment Analysis
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 6 in the
Approx. 33 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
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.
Syllabus - What you will learn from this course
2 hours to complete
Orientation
You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.
2 hours to complete
2 videos (Total 15 min), 5 readings, 2 quizzes
4 hours to complete
Week 1
During this module, you will learn the overall course design, an overview of natural language processing techniques and text representation, which are the foundation for all kinds of text-mining applications, and word association mining with a particular focus on mining one of the two basic forms of word associations (i.e., paradigmatic relations).
4 hours to complete
9 videos (Total 109 min), 1 reading, 2 quizzes
4 hours to complete
Week 2
During this module, you will learn more about word association mining with a particular focus on mining the other basic form of word association (i.e., syntagmatic relations), and start learning topic analysis with a focus on techniques for mining one topic from text.
4 hours to complete
10 videos (Total 116 min), 1 reading, 2 quizzes
10 hours to complete
Week 3
During this module, you will learn topic analysis in depth, including mixture models and how they work, Expectation-Maximization (EM) algorithm and how it can be used to estimate parameters of a mixture model, the basic topic model, Probabilistic Latent Semantic Analysis (PLSA), and how Latent Dirichlet Allocation (LDA) extends PLSA.
10 hours to complete
10 videos (Total 103 min), 2 readings, 3 quizzes
5 hours to complete
Week 4
During this module, you will learn text clustering, including the basic concepts, main clustering techniques, including probabilistic approaches and similarity-based approaches, and how to evaluate text clustering. You will also start learning text categorization, which is related to text clustering, but with pre-defined categories that can be viewed as pre-defining clusters.
5 hours to complete
9 videos (Total 141 min), 1 reading, 2 quizzes
Reviews
- 5 stars67.68%
- 4 stars20.66%
- 3 stars8%
- 2 stars1.89%
- 1 star1.74%
TOP REVIEWS FROM TEXT MINING AND ANALYTICS
by WPAug 22, 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.
by JSJun 6, 2017
The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!
by SGFeb 24, 2017
Very good course thank you
I wish we could have use case applications with high level tools such as R
Thanks a lot again !
by SSJun 20, 2017
My favorite course and my favorite instructor. Highly informative. I wish i want to be a real student of the instructor.
About the Data Mining Specialization
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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