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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Data-Mining
49.249 bereits angemeldet
von
Spezialisierung Data-Mining University of Illinois at Urbana-ChampaignÜber diesen Kurs
25.215 kürzliche Aufrufe
Flexible Fristen
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Zertifikat zur Vorlage
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100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 6 in
Ca. 31 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, Englisch, Spanisch
Kompetenzen, die Sie erwerben
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100 % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Kurs 2 von 6 in
Ca. 31 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Koreanisch, Deutsch, Russisch, 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), 6 Lektüren, 2 Quiz
4 Stunden zum Abschließen
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 Stunden zum Abschließen
6 Videos (Gesamt 94 min), 1 Lektüre, 2 Quiz
4 Stunden zum Abschließen
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 Stunden zum Abschließen
6 Videos (Gesamt 102 min), 1 Lektüre, 2 Quiz
7 Stunden zum Abschließen
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 Stunden zum Abschließen
6 Videos (Gesamt 75 min), 2 Lektüren, 3 Quiz
4 Stunden zum Abschließen
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 Stunden zum Abschließen
7 Videos (Gesamt 88 min), 1 Lektüre, 2 Quiz
Bewertungen
Top-Bewertungen von TEXT RETRIEVAL AND SEARCH ENGINES
von JH20. Sep. 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.
von SX27. März 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
von JW25. Juni 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.
von WP27. Mai 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.
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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