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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Data-Mining
Text Mining and Analytics
Über diesen Kurs
27,408
27,408Kompetenzen, die Sie erwerben
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Lehrplan - Was Sie in diesem Kurs lernen werden
Woche
12 Stunden zum Abschließen
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 Videos (Gesamt 15 min), 5 Lektüren, 2 Quiz
5 Lektüren
Welcome to Text Mining and Analytics!10m
Syllabus15m
About the Discussion Forums15m
Updating your Profile10m
Social Media10m
2 praktische Übungen
Orientation Quiz15m
Pre-Quiz26m
4 Stunden zum Abschließen
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).
9 Videos (Gesamt 109 min), 1 Lektüre, 2 Quiz
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 Lektüre
Week 1 Overview10m
2 praktische Übungen
Week 1 Practice Quiz
Week 1 Quiz
Woche
24 Stunden zum Abschließen
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.
10 Videos (Gesamt 116 min), 1 Lektüre, 2 Quiz
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 Lektüre
Week 2 Overview10m
2 praktische Übungen
Week 2 Practice Quiz
Week 2 Quiz
Woche
310 Stunden zum Abschließen
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 Videos (Gesamt 103 min), 2 Lektüren, 3 Quiz
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 Lektüren
Week 3 Overview10m
Programming Assignments Overview10m
2 praktische Übungen
Week 3 Practice Quiz
Quiz: Week 3 Quiz
Woche
45 Stunden zum Abschließen
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.
9 Videos (Gesamt 141 min), 1 Lektüre, 2 Quiz
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 Lektüre
Week 4 Overview10m
2 praktische Übungen
Week 4 Practice Quiz
Week 4 Quiz
33%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
44%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
12%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Top-Bewertungen
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.
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.
Dozent
ChengXiang Zhai
ProfessorDepartment of Computer Science
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
This Kurs is part of the 100% online Master in Computer Science from University of Illinois at Urbana-Champaign.
If you are admitted to the full program, your courses count towards your degree learning.
Über 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.
Über die Spezialisierung Data-Mining
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.
Courses 2 - 5 of this Specialization form the lecture component of courses in the online Master of Computer Science Degree in Data Science. You can apply to the degree program either before or after you begin the Specialization.
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