Este curso faz parte do Programa de cursos integrados Data Mining
Text Mining and Analytics
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Informações sobre o curso
4.4
309 classificações
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.
Habilidades que você terá
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Programa - O que você aprenderá com este curso
1
Seção
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 vídeos (Total de 15 min), 5 leituras, 2 testes
5 leituras
Welcome to Text Mining and Analytics!10min
Syllabus15min
About the Discussion Forums15min
Updating your Profile10min
Social Media10min
2 exercícios práticos
Orientation Quiz15min
Pre-Quiz26min
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 vídeos (Total de 109 min), 1 leitura, 2 testes
9 videos
1.2 Overview Text Mining and Analytics: Part 211min
1.3 Natural Language Content Analysis: Part 112min
1.4 Natural Language Content Analysis: Part 24min
1.5 Text Representation: Part 110min
1.6 Text Representation: Part 29min
1.7 Word Association Mining and Analysis15min
1.8 Paradigmatic Relation Discovery Part 114min
1.9 Paradigmatic Relation Discovery Part 217min
1 leituras
Week 1 Overview10min
2 exercícios práticos
Week 1 Practice Quizmin
Week 1 Quizmin
2
Seção
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 vídeos (Total de 116 min), 1 leitura, 2 testes
10 videos
2.2 Syntagmatic Relation Discovery: Conditional Entropy11min
2.3 Syntagmatic Relation Discovery: Mutual Information: Part 113min
2.4 Syntagmatic Relation Discovery: Mutual Information: Part 29min
2.5 Topic Mining and Analysis: Motivation and Task Definition7min
2.6 Topic Mining and Analysis: Term as Topic11min
2.7 Topic Mining and Analysis: Probabilistic Topic Models14min
2.8 Probabilistic Topic Models: Overview of Statistical Language Models: Part 110min
2.9 Probabilistic Topic Models: Overview of Statistical Language Models: Part 213min
2.10 Probabilistic Topic Models: Mining One Topic12min
1 leituras
Week 2 Overview10min
2 exercícios práticos
Week 2 Practice Quizmin
Week 2 Quizmin
3
Seção
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 vídeos (Total de 103 min), 2 leituras, 3 testes
10 videos
3.2 Probabilistic Topic Models: Mixture Model Estimation: Part 110min
3.3 Probabilistic Topic Models: Mixture Model Estimation: Part 28min
3.4 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 111min
3.5 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 210min
3.6 Probabilistic Topic Models: Expectation-Maximization Algorithm: Part 36min
3.7 Probabilistic Latent Semantic Analysis (PLSA): Part 110min
3.8 Probabilistic Latent Semantic Analysis (PLSA): Part 210min
3.9 Latent Dirichlet Allocation (LDA): Part 110min
3.10 Latent Dirichlet Allocation (LDA): Part 212min
2 leituras
Week 3 Overview10min
Programming Assignments Overview10min
2 exercícios práticos
Week 3 Practice Quizmin
Quiz: Week 3 Quizmin
4
Seção
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 vídeos (Total de 141 min), 1 leitura, 2 testes
9 videos
4.2 Text Clustering: Generative Probabilistic Models Part 116min
4.3 Text Clustering: Generative Probabilistic Models Part 28min
4.4 Text Clustering: Generative Probabilistic Models Part 314min
4.5 Text Clustering: Similarity-based Approaches17min
4.6 Text Clustering: Evaluation10min
4.7 Text Categorization: Motivation14min
4.8 Text Categorization: Methods11min
4.9 Text Categorization: Generative Probabilistic Models31min
1 leituras
Week 4 Overview10min
2 exercícios práticos
Week 4 Practice Quizmin
Week 4 Quizmin
4.4
33%
83%
17%
Melhores avaliações
por JH•Feb 10th 2017
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.
por DC•Mar 25th 2018
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.
Instrutores
ChengXiang Zhai
ProfessorDepartment of Computer Science
Sobre 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.
Sobre o Programa de cursos integrados 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.
Perguntas Frequentes – FAQ
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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