University of Illinois at Urbana-Champaign

Text Mining and Analytics

This course is part of Data Mining Specialization

Taught in English

Some content may not be translated

ChengXiang Zhai

Instructor: ChengXiang Zhai

70,717 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(724 reviews)

|

92%

33 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

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Assessments

14 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(724 reviews)

|

92%

33 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Mining Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 7 modules in this course

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.

What's included

2 videos5 readings2 quizzes1 plugin

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).

What's included

9 videos1 reading2 quizzes

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.

What's included

10 videos1 reading2 quizzes

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.

What's included

10 videos2 readings2 quizzes1 programming assignment

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.

What's included

9 videos1 reading2 quizzes

During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).

What's included

7 videos1 reading2 quizzes

During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.

What's included

8 videos1 reading2 quizzes1 plugin

Instructor

Instructor ratings
4.6 (52 ratings)
ChengXiang Zhai
University of Illinois at Urbana-Champaign
4 Courses102,587 learners

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