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.
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数据挖掘 专项课程伊利诺伊大学香槟分校课程信息
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第 2 门课程（共 6 门）
完成时间大约为31 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
- Information Retrieval (IR)
- Document Retrieval
- Machine Learning
- Recommender Systems
可灵活调整截止日期
根据您的日程表重置截止日期。
可分享的证书
完成后获得证书
100% 在线
立即开始，按照自己的计划学习。
第 2 门课程（共 6 门）
完成时间大约为31 小时
英语（English）
字幕：阿拉伯语（Arabic）, 法语（French）, （欧洲人讲的）葡萄牙语, 意大利语, 越南语, 韩语, 德语（German）, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
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伊利诺伊大学香槟分校
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.
授课大纲 - 您将从这门课程中学到什么
完成时间为 2 小时
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.
完成时间为 2 小时
2 个视频 （总计 15 分钟）, 6 个阅读材料, 2 个测验
完成时间为 4 小时
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.
完成时间为 4 小时
6 个视频 （总计 94 分钟）, 1 个阅读材料, 2 个测验
完成时间为 4 小时
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.
完成时间为 4 小时
6 个视频 （总计 102 分钟）, 1 个阅读材料, 2 个测验
完成时间为 7 小时
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.
完成时间为 7 小时
6 个视频 （总计 75 分钟）, 2 个阅读材料, 3 个测验
完成时间为 4 小时
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.
完成时间为 4 小时
7 个视频 （总计 88 分钟）, 1 个阅读材料, 2 个测验
审阅
- 5 stars65.52%
- 4 stars23.97%
- 3 stars6.74%
- 2 stars1.65%
- 1 star2.09%
来自文本检索和搜索引擎的热门评论
由 GS 提供May 18, 2020
A bit difficult to complete as the Quiz questions were tougher. But when you go through all, you might feel good.
由 RR 提供Jun 5, 2020
Excellent Course for Computer Science Enthusiastic. Must and Highly recommend course for all Computer Science and Information technology Aspirant
由 HJ 提供Jun 25, 2016
This course is complemented with a software and text (not mandatory) and good explanation with diagrams...
由 SM 提供Apr 17, 2017
More programming assignments would have helped understanding the topics covered in the course better.
关于 数据挖掘 专项课程
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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