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.
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Spezialisierung Data-Mining University of Illinois at Urbana-ChampaignÜber diesen Kurs
23,031 kürzliche Aufrufe
Karriereergebnisse der Lernenden
27%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
33%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
10%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 3 von 6 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 33 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch
Kompetenzen, die Sie erwerben
Data Clustering AlgorithmsText MiningProbabilistic ModelsSentiment Analysis
Karriereergebnisse der Lernenden
27%
nahm einen neuen Beruf nach Abschluss dieser Kurse auf
33%
ziehen Sie für Ihren Beruf greifbaren Nutzen aus diesem Kurs
10%
erhalten Sie eine Gehaltserhöhung oder Beförderung
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 3 von 6 in
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Ca. 33 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Chinesisch (vereinfacht), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch
von
University of Illinois at 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.
Lehrplan - Was Sie in diesem Kurs lernen werden
2 Stunden zum Abschließen
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 Stunden zum Abschließen
2 Videos (Gesamt 15 min), 5 Lektüren, 2 Quiz
5 Lektüren
Welcome to Text Mining and Analytics!10m
Syllabus15m
About the Discussion Forums15m
Updating your Profile10m
Social Media10m
2 praktische Übungen
Orientation Quiz15m
Pre-Quiz30m
4 Stunden zum Abschließen
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 Stunden zum Abschließen
9 Videos (Gesamt 109 min), 1 Lektüre, 2 Quiz
9 Videos
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 Lektüre
Week 1 Overview10m
2 praktische Übungen
Week 1 Practice Quiz1h
Week 1 Quiz1h
4 Stunden zum Abschließen
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 Stunden zum Abschließen
10 Videos (Gesamt 116 min), 1 Lektüre, 2 Quiz
10 Videos
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 Lektüre
Week 2 Overview10m
2 praktische Übungen
Week 2 Practice Quiz1h
Week 2 Quiz1h
10 Stunden zum Abschließen
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 Stunden zum Abschließen
10 Videos (Gesamt 103 min), 2 Lektüren, 3 Quiz
10 Videos
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 Lektüren
Week 3 Overview10m
Programming Assignments Overview10m
2 praktische Übungen
Week 3 Practice Quiz1h
Quiz: Week 3 Quiz1h
5 Stunden zum Abschließen
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 Stunden zum Abschließen
9 Videos (Gesamt 141 min), 1 Lektüre, 2 Quiz
9 Videos
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 Lektüre
Week 4 Overview10m
2 praktische Übungen
Week 4 Practice Quiz1h
Week 4 Quiz1h
Bewertungen
Top-Bewertungen von TEXT MINING AND ANALYTICS
von JH9. Feb. 2017
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.
von DC24. März 2018
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.
von AC11. Feb. 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.
von JS6. Juni 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!
Über den Spezialisierung Data-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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