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.
Este curso faz parte do Programa de cursos integrados Mineração de dadosData Mining
Mineração de Texto e Análise Estatística
oferecido por
Programa de cursos integrados Mineração de dadosData Mining Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign
Informações sobre o curso
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Resultados de carreira do aprendiz
30%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
Habilidades que você terá
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Resultados de carreira do aprendiz
30%
comecei uma nova carreira após concluir estes cursos
38%
consegui um benefício significativo de carreira com este curso
12%
recebi um aumento ou promoção
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 33 horas para completar
Inglês
Legendas: Inglês, Coreano
oferecido por
Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em 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.
Comece a trabalhar rumo ao seu mestrado
Este curso é parte da graduação 100% on-line Master in Computer Science da Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-Champaign.
Caso seja aceito para o programa completo, seus cursos contarão para sua graduação.
Programa - O que você aprenderá com este curso
2 horas para concluir
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 horas para concluir
2 vídeos (Total 15 mín.), 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
4 horas para concluir
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).
4 horas para concluir
9 vídeos (Total 109 mín.), 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 Quiz1h
Week 1 Quiz1h
4 horas para concluir
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.
4 horas para concluir
10 vídeos (Total 116 mín.), 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 Quiz1h
Week 2 Quiz1h
10 horas para concluir
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 horas para concluir
10 vídeos (Total 103 mín.), 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 Quiz1h
Quiz: Week 3 Quiz1h
5 horas para concluir
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.
5 horas para concluir
9 vídeos (Total 141 mín.), 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 Quiz1h
Week 4 Quiz1h
Avaliações
Principais avaliações do MINERAÇÃO DE TEXTO E ANÁLISE ESTATÍSTICA
por JHFeb 9, 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 DCMar 24, 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.
por ACFeb 11, 2017
This is a very good course. I think it provides a very good foundation of text mining and analytics like PLSA and LDA. More advanced research discussed in the last lecture is also very interesting.
por JSJun 6, 2017
The content was very useful, and the preparation of the course denoted much care and preparation by the teacher. I would love to see some modern topics like word embeddings covered in the course!
por WPAug 22, 2017
Most of the lessons are mathematical formulae in which, in my opinion, I need more real case study/practice to make myself clearly understand on how do those formulae perform.
por RCJun 24, 2017
very theoretical. very insightful too. finally got a glimpse how the features that I took for granted took shape ... thanks to the instructor, I love the textbook as well ~
por GIAug 26, 2016
Excellent Module!\n\nA most know & understand unit for all students of Data Science. Enjoyed every aspect of the learning .. Good teaching !!!
por AAApr 18, 2017
The content is really good but the course has too much theory. Mixing it with some practical programming assignments would have been very nice
por GPNov 1, 2017
Outstanding mix of theory and practical applications to help understand the theory. Well organized and excellent presentations. Thank you!
por YDFeb 11, 2019
It is rare to find an online course that explains the statistics and intuition behind text mining and machine learning algorithm!
por SGFeb 24, 2017
Very good course thank you\n\nI wish we could have use case applications with high level tools such as R\n\nThanks a lot again !
por AJJul 14, 2019
This course teaches you the very nitty-gritty details of text mining. It has been an enriching experience for me in this course.
por SSJun 20, 2017
My favorite course and my favorite instructor. Highly informative. I wish i want to be a real student of the instructor.
por MRJul 22, 2017
The workflow is clear and the professor speaks to the students directly about all aspects without skimming the material.
por YLApr 10, 2019
The course was very challenging and i learn a lot of new things from the course, this will help to complete my project.
por CNSep 11, 2017
Good course, but if combined with weekly assignments in python and R it would be even better than any other course.
por NKJul 25, 2019
Initially it was very complex and seemed very theoretical but it all comes together amazingly at the very end.
por ZQOct 21, 2016
Very comprehensive introduction to text mining. It serves well as a First Class to text mining and analytics!
por HAOct 4, 2017
One of the best courses I took in Coursera. Well managed, well presented, valuable information is provided.
por YDJan 1, 2019
Excellent course the provides comprehensive knowledge on Text Mining and Ananlytics in all its dimensions.
Sobre Programa de cursos integrados Mineração de dadosData 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
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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