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.
Este curso faz parte do Programa de cursos integrados Mineração de dadosData Mining
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oferecido por
Programa de cursos integrados Mineração de dadosData Mining Universidade de Illinois em Urbana-ChampaignUniversidade de Illinois em Urbana-ChampaignInformações sobre o curso
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Resultados de carreira do aprendiz
33%
comecei uma nova carreira após concluir estes cursos
50%
consegui um benefício significativo de carreira com este curso
20%
recebi um aumento ou promoção
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 6 no
Aprox. 31 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
Habilidades que você terá
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Resultados de carreira do aprendiz
33%
comecei uma nova carreira após concluir estes cursos
50%
consegui um benefício significativo de carreira com este curso
20%
recebi um aumento ou promoção
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 6 no
Aprox. 31 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Coreano, Alemão, Russo, Inglês, Espanhol
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.
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.), 6 leituras, 2 testes
4 horas para concluir
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 concluir
6 vídeos (Total 94 mín.), 1 leitura, 2 testes
4 horas para concluir
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 concluir
6 vídeos (Total 102 mín.), 1 leitura, 2 testes
7 horas para concluir
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 concluir
6 vídeos (Total 75 mín.), 2 leituras, 3 testes
4 horas para concluir
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 concluir
7 vídeos (Total 88 mín.), 1 leitura, 2 testes
Avaliações
Principais avaliações do RECUPERAÇÃO DE TEXTO E MOTORES DE BUSCA
por JH20 de Set 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 Mai 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.
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.
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