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.
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
50%
comecei uma nova carreira após concluir estes cursos
67%
consegui um benefício significativo de carreira com este curso
29%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 31 horas para completar
Inglês
Legendas: Inglês, Coreano
Habilidades que você terá
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Resultados de carreira do aprendiz
50%
comecei uma nova carreira após concluir estes cursos
67%
consegui um benefício significativo de carreira com este curso
29%
recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 2 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Aprox. 31 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.), 6 leituras, 2 testes
6 leituras
Welcome to Text Retrieval and Search Engines!10min
Syllabus10min
About the Discussion Forums10min
Updating your Profile10min
Social Media10min
Course Errata10min
2 exercícios práticos
Orientation Quiz15min
Pre-Quiz30min
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
6 videos
Lesson 1.2: Text Access9min
Lesson 1.3: Text Retrieval Problem26min
Lesson 1.4: Overview of Text Retrieval Methods10min
Lesson 1.5: Vector Space Model - Basic Idea9min
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17min
1 leituras
Week 1 Overview10min
2 exercícios práticos
Week 1 Practice Quiz1h
Week 1 Quiz1h
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
6 videos
Lesson 2.2: TF Transformation9min
Lesson 2.3: Doc Length Normalization18min
Lesson 2.4: Implementation of TR Systems21min
Lesson 2.5: System Implementation - Inverted Index Construction18min
Lesson 2.6: System Implementation - Fast Search17min
1 leituras
Week 2 Overview10min
2 exercícios práticos
Week 2 Practice Quiz1h
Week 2 Quiz1h
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
6 videos
Lesson 3.2: Evaluation of TR Systems - Basic Measures12min
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115min
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210min
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10min
Lesson 3.6: Evaluation of TR Systems - Practical Issues15min
2 leituras
Week 3 Overview10min
Programming Assignments Overview10min
2 exercícios práticos
Week 3 Practice Quiz1h
Week 3 Quiz1h
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
7 videos
Lesson 4.2: Statistical Language Model17min
Lesson 4.3: Query Likelihood Retrieval Function12min
Lesson 4.4: Statistical Language Model - Part 112min
Lesson 4.5: Statistical Language Model - Part 29min
Lesson 4.6: Smoothing Methods - Part 19min
Lesson 4.7: Smoothing Methods - Part 213min
1 leituras
Week 4 Overview10min
2 exercícios práticos
Week 4 Practice Quiz1h
Week 4 Quiz1h
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.
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
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The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
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