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.
제공자:
데이터 마이닝 특화 과정일리노이대학교 어버너-섐페인캠퍼스이 강좌에 대하여
최근 조회 57,370회
학습자 경력 결과
30%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
38%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
12%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 33시간 필요
영어
자막: 영어, 한국어
귀하가 습득할 기술
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
학습자 경력 결과
30%
가 이 강좌를 수료한 후 새로운 커리어를 시작함
38%
가 이 강좌를 통해 확실한 경력상 이점을 얻음
12%
가 급여 인상 또는 승진 성취
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 6개 강좌 중 3번째 강좌:
유동적 마감일
일정에 따라 마감일을 재설정합니다.
완료하는 데 약 33시간 필요
영어
자막: 영어, 한국어
제공자:
일리노이대학교 어버너-섐페인캠퍼스
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.
석사 학위 취득 시작
This 강좌 is part of the 100% online Master in Computer Science from 일리노이대학교 어버너-섐페인캠퍼스.
If you are admitted to the full program, your courses count towards your degree learning.
강의 계획 - 이 강좌에서 배울 내용
완료하는 데 2시간 필요
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.
완료하는 데 2시간 필요
2개 동영상 (총 15분), 5 개의 읽기 자료, 2 개의 테스트
5개의 읽기 자료
Welcome to Text Mining and Analytics!10m
Syllabus15m
About the Discussion Forums15m
Updating your Profile10m
Social Media10m
2개 연습문제
Orientation Quiz15m
Pre-Quiz26m
완료하는 데 4시간 필요
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).
완료하는 데 4시간 필요
9개 동영상 (총 109분), 1 개의 읽기 자료, 2 개의 테스트
9개의 동영상
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
1개의 읽기 자료
Week 1 Overview10m
2개 연습문제
Week 1 Practice Quiz1시간
Week 1 Quiz1시간
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
10개 동영상 (총 116분), 1 개의 읽기 자료, 2 개의 테스트
10개의 동영상
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
1개의 읽기 자료
Week 2 Overview10m
2개 연습문제
Week 2 Practice Quiz1시간
Week 2 Quiz1시간
완료하는 데 10시간 필요
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.
완료하는 데 10시간 필요
10개 동영상 (총 103분), 2 개의 읽기 자료, 3 개의 테스트
10개의 동영상
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
2개의 읽기 자료
Week 3 Overview10m
Programming Assignments Overview10m
2개 연습문제
Week 3 Practice Quiz1시간
Quiz: Week 3 Quiz1시간
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
9개 동영상 (총 141분), 1 개의 읽기 자료, 2 개의 테스트
9개의 동영상
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
1개의 읽기 자료
Week 4 Overview10m
2개 연습문제
Week 4 Practice Quiz1시간
Week 4 Quiz1시간
검토
TEXT MINING AND ANALYTICS의 최상위 리뷰
JH 제공2017년 2월 9일
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.
DC 제공2018년 3월 24일
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.
AC 제공2017년 2월 11일
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.
JS 제공2017년 6월 6일
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!
데이터 마이닝 특화 과정 정보
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.
자주 묻는 질문
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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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