This course is part of the Data Mining Specialization

4.5
stars
932 ratings

ChengXiang Zhai

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Data Mining SpecializationUniversity of Illinois at Urbana-Champaign

About this Course

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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. 
This course will cover search engine technologies, which play an important role in any data mining applications involving text data for two reasons. First, while the raw data may be large for any particular problem, it is often a relatively small subset of the data that are relevant, and a search engine is an essential tool for quickly discovering a small subset of relevant text data in a large text collection. Second, search engines are needed to help analysts interpret any patterns discovered in the data by allowing them to examine the relevant original text data to make sense of any discovered pattern. You will learn the basic concepts, principles, and the major techniques in text retrieval, which is the underlying science of search engines.

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Coursera Labs

Includes hands on learning projects.

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Course 2 of 6 in the

Data Mining Specialization

Approx. 30 hours to complete

English

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Skills you will gain

Information Retrieval (IR)
Document Retrieval
Machine Learning
Recommender Systems

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 6 in the

Data Mining Specialization

Approx. 30 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.38/5 (83 Ratings)

ChengXiang Zhai

Professor

Department of Computer Science

97,607 Learners

4 Courses

Offered by

University of Illinois at Urbana-Champaign

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Syllabus - What you will learn from this course

Content Rating90%(3,062 ratings)

Week1

Week 1

2 hours to complete

Orientation

2 hours to complete

2 videos (Total 15 min), 6 readings, 2 quizzes

4 hours to complete

Week 1

4 hours to complete

6 videos (Total 94 min), 1 reading, 2 quizzes

Week2

Week 2

4 hours to complete

Week 2

4 hours to complete

6 videos (Total 102 min), 1 reading, 2 quizzes

Week3

Week 3

7 hours to complete

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 hours to complete

6 videos (Total 75 min), 2 readings, 3 quizzes

6 videos

Lesson 3.1: Evaluation of TR Systems10mLesson 3.2: Evaluation of TR Systems - Basic Measures12mLesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115mLesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210mLesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10mLesson 3.6: Evaluation of TR Systems - Practical Issues15m

2 readings

Week 3 Overview10mProgramming Assignments Overview10m

2 practice exercises

Week 3 Practice Quiz1hWeek 3 Quiz1h

Week4

Week 4

4 hours to complete

Week 4

4 hours to complete

7 videos (Total 88 min), 1 reading, 2 quizzes

7 videos

Lesson 4.1: Probabilistic Retrieval Model - Basic Idea12mLesson 4.2: Statistical Language Model17mLesson 4.3: Query Likelihood Retrieval Function12mLesson 4.4: Statistical Language Model - Part 112mLesson 4.5: Statistical Language Model - Part 29mLesson 4.6: Smoothing Methods - Part 19mLesson 4.7: Smoothing Methods - Part 213m

1 reading

Week 4 Overview10m

2 practice exercises

Week 4 Practice Quiz1hWeek 4 Quiz1h

Week5

Week 5

4 hours to complete

Week 5

4 hours to complete

8 videos (Total 99 min), 1 reading, 2 quizzes

8 videos

Lesson 5.1: Feedback in Text Retrieval6mLesson 5.2: Feedback in Vector Space Model - Rocchio12mLesson 5.3: Feedback in Text Retrieval - Feedback in LM19mLesson 5.4: Web Search: Introduction & Web Crawler11mLesson 5.5: Web Indexing17mLesson 5.6: Link Analysis - Part 19mLesson 5.7: Link Analysis - Part 217mLesson 5.8: Link Analysis - Part 35m

1 reading

Week 5 Overview10m

2 practice exercises

Week 5 Practice Quiz1hWeek 5 Quiz1h

Week6

Week 6

7 hours to complete

Week 6

7 hours to complete

10 videos (Total 91 min), 1 reading, 3 quizzes

10 videos

Lesson 6.1: Learning to Rank - Part 15mLesson 6.2: Learning to Rank - Part 210mLesson 6.3: Learning to Rank - Part 34mLesson 6.4: Future of Web Search13mLesson 6.5: Recommender Systems: Content-Based Filtering - Part 112mLesson 6.6: Recommender Systems: Content-Based Filtering - Part 210mLesson 6.7: Recommender Systems: Collaborative Filtering - Part 16mLesson 6.8: Recommender Systems: Collaborative Filtering - Part 212mLesson 6.9: Recommender Systems: Collaborative Filtering - Part 34mLesson 6.10: Course Summary9m

1 reading

Week 6 Overview10m

2 practice exercises

Week 6 Practice Quiz1hWeek 6 Quiz1h

Reviews

4.5

192 reviews

5 stars
65.91%
4 stars
23.79%
3 stars
6.64%
2 stars
1.60%
1 star
2.03%

TOP REVIEWS FROM TEXT RETRIEVAL AND SEARCH ENGINES

by MSDec 29, 2020

Great introductory course. It has opened my eyes to new challenges that face information retrieval. DON"T TAKE THIS COURSE IF YOU WANT TO LEARN elasticsearch.

by AGOct 15, 2019

Course was well taught the instructor's explanation of the topics was very comprehensive. Overall satisfied with the experience

by GSMay 19, 2020

A bit difficult to complete as the Quiz questions were tougher. But when you go through all, you might feel good.

by JWJun 26, 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.

View all reviews

About the Data Mining Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Text Retrieval and Search Engines

About this Course

Skills you will gain