About 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
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

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

Offered by

Placeholder

University of Illinois at Urbana-Champaign

Syllabus - What you will learn from this course

Content RatingThumbs Up91%(2,675 ratings)Info
Week
1

Week 1

2 hours to complete

Orientation

2 hours to complete
2 videos (Total 15 min), 5 readings, 2 quizzes
2 videos
Course Prerequisites & Completion6m
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

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
Week
2

Week 2

4 hours to complete

Week 2

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
Week
3

Week 3

10 hours to complete

Week 3

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
Week
4

Week 4

5 hours to complete

Week 4

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

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