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
39,490 次近期查看
学生职业成果
50%
完成这些课程后已开始新的职业生涯
67%
通过此课程获得实实在在的工作福利
29%
加薪或升职
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）
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为31 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 俄语（Russian）, 英语（English）, 西班牙语（Spanish）
您将获得的技能
Information Retrieval (IR)Document RetrievalMachine LearningRecommender Systems
学生职业成果
50%
完成这些课程后已开始新的职业生涯
67%
通过此课程获得实实在在的工作福利
29%
加薪或升职
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）
可灵活调整截止日期
根据您的日程表重置截止日期。
完成时间大约为31 小时
英语（English）
字幕：法语（French）, 巴西葡萄牙语, 韩语, 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.
教学大纲 - 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
6 个阅读材料
Welcome to Text Retrieval and Search Engines!10分钟
Syllabus10分钟
About the Discussion Forums10分钟
Updating your Profile10分钟
Social Media10分钟
Course Errata10分钟
2 个练习
Orientation Quiz15分钟
Pre-Quiz30分钟
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
6 个视频
Lesson 1.2: Text Access9分钟
Lesson 1.3: Text Retrieval Problem26分钟
Lesson 1.4: Overview of Text Retrieval Methods10分钟
Lesson 1.5: Vector Space Model - Basic Idea9分钟
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation17分钟
1 个阅读材料
Week 1 Overview10分钟
2 个练习
Week 1 Practice Quiz1小时
Week 1 Quiz1小时
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
6 个视频
Lesson 2.2: TF Transformation9分钟
Lesson 2.3: Doc Length Normalization18分钟
Lesson 2.4: Implementation of TR Systems21分钟
Lesson 2.5: System Implementation - Inverted Index Construction18分钟
Lesson 2.6: System Implementation - Fast Search17分钟
1 个阅读材料
Week 2 Overview10分钟
2 个练习
Week 2 Practice Quiz1小时
Week 2 Quiz1小时
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
6 个视频
Lesson 3.2: Evaluation of TR Systems - Basic Measures12分钟
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115分钟
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210分钟
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10分钟
Lesson 3.6: Evaluation of TR Systems - Practical Issues15分钟
2 个阅读材料
Week 3 Overview10分钟
Programming Assignments Overview10分钟
2 个练习
Week 3 Practice Quiz1小时
Week 3 Quiz1小时
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
7 个视频
Lesson 4.2: Statistical Language Model17分钟
Lesson 4.3: Query Likelihood Retrieval Function12分钟
Lesson 4.4: Statistical Language Model - Part 112分钟
Lesson 4.5: Statistical Language Model - Part 29分钟
Lesson 4.6: Smoothing Methods - Part 19分钟
Lesson 4.7: Smoothing Methods - Part 213分钟
1 个阅读材料
Week 4 Overview10分钟
2 个练习
Week 4 Practice Quiz1小时
Week 4 Quiz1小时
审阅
来自文本检索和搜索引擎的热门评论
由 JH 提供Sep 20, 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.
由 SX 提供Mar 27, 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
由 JW 提供Jun 25, 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.
由 WP 提供May 27, 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.
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.
常见问题
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我订阅此专项课程后会得到什么？
您注册课程后，将有权访问专项课程中的所有课程，并且会在完成课程后获得证书。您的电子课程证书将添加到您的成就页中，您可以通过该页打印您的课程证书或将其添加到您的领英档案中。如果您只想阅读和查看课程内容，可以免费旁听课程。
Is financial aid available?
是的，Coursera 可以为无法承担费用的学生提供助学金。通过点击左侧“注册”按钮下的“助学金”链接可以申请助学金。您可以根据屏幕提示完成申请，申请获批后会收到通知。您需要针对专项课程中的每一门课程完成上述步骤，包括毕业项目。了解更多。
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