UIUC - Text Mining and Analytics
- Offered byCoursera
Text Mining and Analytics at Coursera Overview
Duration | 33 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Official Website | Explore Free Course  |
Credential | Certificate |
Text Mining and Analytics at Coursera Highlights
- 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.
- Earn a shareable certificate upon completion.
Text Mining and Analytics at Coursera Course details
- 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.
Text Mining and Analytics at Coursera Curriculum
Orientation
Introduction to Text Mining and Analytics
Course Prerequisites & Completion
Welcome to Text Mining and Analytics!
Syllabus
About the Discussion Forums
Updating your Profile
Social Media
Orientation Quiz
Pre-Quiz
1.1 Overview Text Mining and Analytics: Part 1
1.2 Overview Text Mining and Analytics: Part 2
1.3 Natural Language Content Analysis: Part 1
1.4 Natural Language Content Analysis: Part 2
1.5 Text Representation: Part 1
1.6 Text Representation: Part 2
1.7 Word Association Mining and Analysis
1.8 Paradigmatic Relation Discovery Part 1
1.9 Paradigmatic Relation Discovery Part 2
Week 1 Overview
Week 1 Practice Quiz
Week 1 Quiz
Week 2
2.1 Syntagmatic Relation Discovery: Entropy
2.2 Syntagmatic Relation Discovery: Conditional Entropy
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 1
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 2
2.5 Topic Mining and Analysis: Motivation and Task Definition
2.6 Topic Mining and Analysis: Term as Topic
2.7 Topic Mining and Analysis: Probabilistic Topic Models
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 1
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 2
2.10 Probabilistic Topic Models: Mining One Topic
Week 2 Overview
Week 2 Practice Quiz
Week 2 Quiz
Week 3
3.1 Probabilistic Topic Models: Mixture of Unigram Language Models
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 1
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 2
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 1
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 2
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 3
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 1
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 2
3.9 Latent Dirichlet Allocation (LDA): Part 1
3.10 Latent Dirichlet Allocation (LDA): Part 2
Week 3 Overview
Programming Assignments Overview
Week 3 Practice Quiz
Quiz: Week 3 Quiz
Week 4
4.1 Text Clustering: Motivation
4.2 Text Clustering: Generative Probabilistic Models Part 1
4.3 Text Clustering: Generative Probabilistic Models Part 2
4.4 Text Clustering: Generative Probabilistic Models Part 3
4.5 Text Clustering: Similarity-based Approaches
4.6 Text Clustering: Evaluation
4.7 Text Categorization: Motivation
4.8 Text Categorization: Methods
4.9 Text Categorization: Generative Probabilistic Models
Week 4 Overview
Week 4 Practice Quiz
Week 4 Quiz
Week 5
5.1 Text Categorization: Discriminative Classifier Part 1
5.2 Text Categorization: Discriminative Classifier Part 2
5.3 Text Categorization: Evaluation Part 1
5.4 Text Categorization: Evaluation Part 2
5.5 Opinion Mining and Sentiment Analysis: Motivation
5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification
5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression
Week 5 Overview
Week 5 Practice Quiz
Week 5 Quiz
Week 6
6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 1
6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 2
6.3 Text-Based Prediction
6.4 Contextual Text Mining: Motivation
6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis
6.6 Contextual Text Mining: Mining Topics with Social Network Context
6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision
6.8 Course Summary
Week 6 Overview
Week 6 Practice Quiz
Week 6 Quiz
Text Mining and Analytics at Coursera Admission Process
Important Dates
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