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.
Offered By
Data Mining SpecializationUniversity of Illinois at Urbana-ChampaignAbout this Course
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Learner Career Outcomes
27%
started a new career after completing these courses
33%
got a tangible career benefit from this course
10%
got a pay increase or promotion
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Course 3 of 6 in the
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Approx. 33 hours to complete
English
Subtitles: Arabic, French, Portuguese (Brazilian), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Chinese
Skills you will gain
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Learner Career Outcomes
27%
started a new career after completing these courses
33%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Approx. 33 hours to complete
English
Subtitles: Arabic, French, Portuguese (Brazilian), Italian, Vietnamese, Korean, German, Russian, Turkish, English, Spanish, Chinese
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
5 readings
Welcome to Text Mining and Analytics!10m
Syllabus15m
About the Discussion Forums15m
Updating your Profile10m
Social Media10m
2 practice exercises
Orientation Quiz15m
Pre-Quiz30m
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
9 videos
1.2 Overview Text Mining and Analytics: Part 211m
1.3 Natural Language Content Analysis: Part 112m
1.4 Natural Language Content Analysis: Part 24m
1.5 Text Representation: Part 110m
1.6 Text Representation: Part 29m
1.7 Word Association Mining and Analysis15m
1.8 Paradigmatic Relation Discovery Part 114m
1.9 Paradigmatic Relation Discovery Part 217m
1 reading
Week 1 Overview10m
2 practice exercises
Week 1 Practice Quiz1h
Week 1 Quiz1h
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 videos
2.2 Syntagmatic Relation Discovery: Conditional Entropy11m
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113m
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29m
2.5 Topic Mining and Analysis: Motivation and Task Definition7m
2.6 Topic Mining and Analysis: Term as Topic11m
2.7 Topic Mining and Analysis: Probabilistic Topic Models14m
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110m
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213m
2.10 Probabilistic Topic Models: Mining One Topic12m
1 reading
Week 2 Overview10m
2 practice exercises
Week 2 Practice Quiz1h
Week 2 Quiz1h
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
10 videos
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110m
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28m
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111m
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210m
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36m
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110m
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210m
3.9 Latent Dirichlet Allocation (LDA): Part 110m
3.10 Latent Dirichlet Allocation (LDA): Part 212m
2 readings
Week 3 Overview10m
Programming Assignments Overview10m
2 practice exercises
Week 3 Practice Quiz1h
Quiz: Week 3 Quiz1h
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
9 videos
4.2 Text Clustering: Generative Probabilistic Models Part 116m
4.3 Text Clustering: Generative Probabilistic Models Part 28m
4.4 Text Clustering: Generative Probabilistic Models Part 314m
4.5 Text Clustering: Similarity-based Approaches17m
4.6 Text Clustering: Evaluation10m
4.7 Text Categorization: Motivation14m
4.8 Text Categorization: Methods11m
4.9 Text Categorization: Generative Probabilistic Models31m
1 reading
Week 4 Overview10m
2 practice exercises
Week 4 Practice Quiz1h
Week 4 Quiz1h
Reviews
TOP REVIEWS FROM TEXT MINING AND ANALYTICS
by JHFeb 9, 2017
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.
by DCMar 24, 2018
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.
by ACFeb 11, 2017
This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.
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!
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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