Este curso forma parte de Programa especializado: minería de datos

4.5
estrellas
683 calificaciones

92 %

ChengXiang Zhai

65.676 ya inscrito

ofrecido por

Programa especializado: minería de datos Universidad de Illinois en Urbana-Champaign

Acerca de este Curso

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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.

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Curso 3 de 6 en

Programa especializado: minería de datos

Aprox. 33 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

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Habilidades que obtendrás

Data Clustering Algorithms
Text Mining
Probabilistic Models
Sentiment Analysis

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 6 en

Programa especializado: minería de datos

Aprox. 33 horas para completar

Inglés (English)

Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructor

Calificación del instructor

4,53/5 (47 calificaciones)

ChengXiang Zhai

Professor

Department of Computer Science

94.057 alumnos

4 cursos

ofrecido por

Universidad de Illinois en 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.

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Programa - Qué aprenderás en este curso

Calificación del contenido92%(2,936 calificaciones)

Semana

Semana 1

2 horas para completar

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 completar

2 videos (Total 15 minutos), 5 lecturas, 2 cuestionarios

4 horas para completar

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 completar

9 videos (Total 109 minutos), 1 lectura, 2 cuestionarios

9 videos

1.1 Overview Text Mining and Analytics: Part 111m

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 lectura

Week 1 Overview10m

2 ejercicios de práctica

Week 1 Practice Quiz1h

Week 1 Quiz1h

Semana

Semana 2

4 horas para completar

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 completar

10 videos (Total 116 minutos), 1 lectura, 2 cuestionarios

10 videos

2.1 Syntagmatic Relation Discovery: Entropy11m

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 lectura

Week 2 Overview10m

2 ejercicios de práctica

Week 2 Practice Quiz1h

Week 2 Quiz1h

Semana

Semana 3

10 horas para completar

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 completar

10 videos (Total 103 minutos), 2 lecturas, 3 cuestionarios

10 videos

3.1 Probabilistic Topic Models: Mixture of Unigram Language Models12m

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 lecturas

Week 3 Overview10m

Programming Assignments Overview10m

2 ejercicios de práctica

Week 3 Practice Quiz1h

Quiz: Week 3 Quiz1h

Semana

Semana 4

5 horas para completar

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 completar

9 videos (Total 141 minutos), 1 lectura, 2 cuestionarios

9 videos

4.1 Text Clustering: Motivation15m

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 lectura

Week 4 Overview10m

2 ejercicios de práctica

Week 4 Practice Quiz1h

Week 4 Quiz1h

Semana

Semana 5

4 horas para completar

Week 5

During this module, you will continue learning about various methods for text categorization, including multiple methods classified under discriminative classifiers, and you will also learn sentiment analysis and opinion mining, including a detailed introduction to a particular technique for sentiment classification (i.e., ordinal regression).

4 horas para completar

7 videos (Total 121 minutos), 1 lectura, 2 cuestionarios

7 videos

5.1 Text Categorization: Discriminative Classifier Part 120m

5.2 Text Categorization: Discriminative Classifier Part 231m

5.3 Text Categorization: Evaluation Part 114m

5.4 Text Categorization: Evaluation Part 210m

5.5 Opinion Mining and Sentiment Analysis: Motivation17m

5.6 Opinion Mining and Sentiment Analysis: Sentiment Classification11m

5.7 Opinion Mining and Sentiment Analysis: Ordinal Logistic Regression13m

1 lectura

Week 5 Overview10m

2 ejercicios de práctica

Week 5 Practice Quiz1h

Week 5 Quiz1h

Semana

Semana 6

4 horas para completar

Week 6

During this module, you will continue learning about sentiment analysis and opinion mining with a focus on Latent Aspect Rating Analysis (LARA), and you will learn about techniques for joint mining of text and non-text data, including contextual text mining techniques for analyzing topics in text in association with various context information such as time, location, authors, and sources of data. You will also see a summary of the entire course.

4 horas para completar

8 videos (Total 120 minutos), 1 lectura, 2 cuestionarios

8 videos

6.1 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 115m

6.2 Opinion Mining and Sentiment Analysis: Latent Aspect Rating Analysis Part 214m

6.3 Text-Based Prediction12m

6.4 Contextual Text Mining: Motivation6m

6.5 Contextual Text Mining: Contextual Probabilistic Latent Semantic Analysis17m

6.6 Contextual Text Mining: Mining Topics with Social Network Context14m

6.7 Contextual Text Mining: Mining Casual Topics with Time Series Supervision19m

6.8 Course Summary18m

1 lectura

Week 6 Overview10m

2 ejercicios de práctica

Week 6 Practice Quiz1h

Week 6 Quiz1h

Reseñas

4.5

142 reseña

5 stars
67,73 %
4 stars
20,63 %
3 stars
7,99 %
2 stars
1,88 %
1 star
1,74 %

Principales reseñas sobre TEXT MINING AND ANALYTICS

por SG24 de feb. de 2017

Very good course thank you

I wish we could have use case applications with high level tools such as R

Thanks a lot again !

por AC11 de feb. de 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 LV26 de jul. de 2020

Well taught course, would have preferred if went further into topics like opinion mining (even if we had optional lectures or assignments). However, definitely a fun course to learn a lot.

por JS6 de jun. de 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!

Ver todas las reseñas

Acerca de Programa especializado: minería de datos

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.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Text Mining and Analytics

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Habilidades que obtendrás