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Text Mining and Analytics

Approx. 33 hours to complete

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Course Summary

This course in text mining will teach you how to extract useful information and insights from large amounts of unstructured text data using various techniques and tools.

Key Learning Points

Learn the basics of text mining and its applications
Understand the different techniques and tools used in text mining
Gain practical experience in text mining through hands-on projects

Learning Outcomes

Ability to extract valuable insights from unstructured text data
Proficiency in using text mining tools and techniques
Hands-on experience in text mining through projects

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming
Familiarity with data analysis

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced

Similar Courses

Applied Text Mining and Sentiment Analysis
Text Retrieval and Search Engines
Introduction to Natural Language Processing

Related Education Paths

Related Books

Description

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.

Outline

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

Week 1
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

Summary of User Reviews

Learn the art of text mining with Coursera's course on text mining. Users have praised this course for its comprehensive content that is easy to understand.

Key Aspect Users Liked About This Course

Comprehensive content that is easy to understand.

Pros from User Reviews

In-depth coverage of text mining techniques and applications
Clear explanations and examples that make the course easy to follow
Interactive assignments and quizzes that reinforce learning
Access to a knowledgeable community of learners and instructors

Cons from User Reviews

Some users felt that the course was too basic and lacked advanced content
A few users experienced technical difficulties with the platform
The course may be challenging for those with no prior experience in data analysis
The workload may be overwhelming for those with a busy schedule

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