Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. Text data are unique in that they are usually generated directly by humans rather than a computer system or sensors, and are thus especially valuable for discovering knowledge about people’s opinions and preferences, in addition to many other kinds of knowledge that we encode in text.
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Programa Especializado - minería de datos Universidad de Illinois en Urbana-ChampaignAcerca de este Curso
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Resultados profesionales del estudiante
50%
comenzó una nueva carrera después de completar estos cursos
60%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
25%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 6 en
Fechas límite flexibles
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Aprox. 31 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (de Brasil), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish), Chino (Chinese)
Habilidades que obtendrás
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Resultados profesionales del estudiante
50%
comenzó una nueva carrera después de completar estos cursos
60%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
25%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 6 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Aprox. 31 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (de Brasil), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish), Chino (Chinese)
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.
Programa - Qué aprenderás en este curso
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), 6 lecturas, 2 cuestionarios
6 lecturas
Welcome to Text Retrieval and Search Engines!10m
Syllabus10m
About the Discussion Forums10m
Updating your Profile10m
Social Media10m
Course Errata10m
2 ejercicios de práctica
Orientation Quiz15m
Pre-Quiz30m
4 horas para completar
Week 1
During this week's lessons, you will learn of natural language processing techniques, which are the foundation for all kinds of text-processing applications, the concept of a retrieval model, and the basic idea of the vector space model.
4 horas para completar
6 videos (Total 94 minutos), 1 lectura, 2 cuestionarios
6 videos
Lesson 1.2: Text Access9m
Lesson 1.3: Text Retrieval Problem26m
Lesson 1.4: Overview of Text Retrieval Methods10m
Lesson 1.5: Vector Space Model - Basic Idea9m
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17m
1 lectura
Week 1 Overview10m
2 ejercicios de práctica
Week 1 Practice Quiz1h
Week 1 Quiz1h
4 horas para completar
Week 2
In this week's lessons, you will learn how the vector space model works in detail, the major heuristics used in designing a retrieval function for ranking documents with respect to a query, and how to implement an information retrieval system (i.e., a search engine), including how to build an inverted index and how to score documents quickly for a query.
4 horas para completar
6 videos (Total 102 minutos), 1 lectura, 2 cuestionarios
6 videos
Lesson 2.2: TF Transformation9m
Lesson 2.3: Doc Length Normalization18m
Lesson 2.4: Implementation of TR Systems21m
Lesson 2.5: System Implementation - Inverted Index Construction18m
Lesson 2.6: System Implementation - Fast Search17m
1 lectura
Week 2 Overview10m
2 ejercicios de práctica
Week 2 Practice Quiz1h
Week 2 Quiz1h
7 horas para completar
Week 3
In this week's lessons, you will learn how to evaluate an information retrieval system (a search engine), including the basic measures for evaluating a set of retrieved results and the major measures for evaluating a ranked list, including the average precision (AP) and the normalized discounted cumulative gain (nDCG), and practical issues in evaluation, including statistical significance testing and pooling.
7 horas para completar
6 videos (Total 75 minutos), 2 lecturas, 3 cuestionarios
6 videos
Lesson 3.2: Evaluation of TR Systems - Basic Measures12m
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115m
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210m
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10m
Lesson 3.6: Evaluation of TR Systems - Practical Issues15m
2 lecturas
Week 3 Overview10m
Programming Assignments Overview10m
2 ejercicios de práctica
Week 3 Practice Quiz1h
Week 3 Quiz1h
4 horas para completar
Week 4
In this week's lessons, you will learn probabilistic retrieval models and statistical language models, particularly the detail of the query likelihood retrieval function with two specific smoothing methods, and how the query likelihood retrieval function is connected with the retrieval heuristics used in the vector space model.
4 horas para completar
7 videos (Total 88 minutos), 1 lectura, 2 cuestionarios
7 videos
Lesson 4.2: Statistical Language Model17m
Lesson 4.3: Query Likelihood Retrieval Function12m
Lesson 4.4: Statistical Language Model - Part 112m
Lesson 4.5: Statistical Language Model - Part 29m
Lesson 4.6: Smoothing Methods - Part 19m
Lesson 4.7: Smoothing Methods - Part 213m
1 lectura
Week 4 Overview10m
2 ejercicios de práctica
Week 4 Practice Quiz1h
Week 4 Quiz1h
Reseñas
Principales reseñas sobre TEXT RETRIEVAL AND SEARCH ENGINES
por JH20 de sep. de 2016
Great course for those trying to understand how ro analyse and process text data. It has the right amount of tools to help you understand the basics of information retrieval and search engines.
por SX27 de mar. de 2018
I have learned a lot of concepts through this course, but at a shallow level. It is a great introduction course to IR. It can be improved by adding more programming tasks for hands-on exercise
por JW25 de jun. de 2020
I will keep the last star for not using a more cutting edge programming language, e.g. python. MeTa is not really helpful in the business world and that discounts the value of this course.
por WP27 de may. de 2017
I found that there were a lot of mathematical function in this course. I need to see the examples of how are those functions applied in the real programming which support the business.
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?
¿Qué recibiré si me suscribo a este Programa especializado?
Is financial aid available?
¿Recibiré crédito universitario por completar el Curso?
¿Tienes más preguntas? Visita el Centro de Ayuda al Alumno.
