This course is part of the Data Mining Specialization

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stars
905 ratings

ChengXiang Zhai

53,083 already enrolled

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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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Earn a Certificate upon completion

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Course 2 of 6 in the

Data Mining Specialization

Approx. 31 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

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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.

Course 2 of 6 in the

Data Mining Specialization

Approx. 31 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

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

Try Coursera for Business

Instructor

Instructor rating

4.37/5 (81 Ratings)

ChengXiang Zhai

Professor

Department of Computer Science

94,057 Learners

4 Courses

Offered by

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.

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Syllabus - What you will learn from this course

Content Rating90%(2,974 ratings)

Week

Week 1

2 hours to complete

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 hours to complete

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

4 hours to complete

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 hours to complete

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

Week

Week 2

4 hours to complete

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 hours to complete

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

Week

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 Systems10m

Lesson 3.2: Evaluation of TR Systems - Basic Measures12m

Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 115m

Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 210m

Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements10m

Lesson 3.6: Evaluation of TR Systems - Practical Issues15m

2 readings

Week 3 Overview10m

Programming Assignments Overview10m

2 practice exercises

Week 3 Practice Quiz1h

Week 3 Quiz1h

Week

Week 4

4 hours to complete

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 hours to complete

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

7 videos

Lesson 4.1: Probabilistic Retrieval Model - Basic Idea12m

Lesson 4.2: Statistical Language Model17m

Lesson 4.3: Query Likelihood Retrieval Function12m

Lesson 4.4: Statistical Language Model - Part 112m

Lesson 4.5: Statistical Language Model - Part 29m

Lesson 4.6: Smoothing Methods - Part 19m

Lesson 4.7: Smoothing Methods - Part 213m

1 reading

Week 4 Overview10m

2 practice exercises

Week 4 Practice Quiz1h

Week 4 Quiz1h

Week

Week 5

4 hours to complete

Week 5

In this week's lessons, you will learn feedback techniques in information retrieval, including the Rocchio feedback method for the vector space model, and a mixture model for feedback with language models. You will also learn how web search engines work, including web crawling, web indexing, and how links between web pages can be leveraged to score web pages.

4 hours to complete

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

8 videos

Lesson 5.1: Feedback in Text Retrieval6m

Lesson 5.2: Feedback in Vector Space Model - Rocchio12m

Lesson 5.3: Feedback in Text Retrieval - Feedback in LM19m

Lesson 5.4: Web Search: Introduction & Web Crawler11m

Lesson 5.5: Web Indexing17m

Lesson 5.6: Link Analysis - Part 19m

Lesson 5.7: Link Analysis - Part 217m

Lesson 5.8: Link Analysis - Part 35m

1 reading

Week 5 Overview10m

2 practice exercises

Week 5 Practice Quiz1h

Week 5 Quiz1h

Week

Week 6

7 hours to complete

Week 6

In this week's lessons, you will learn how machine learning can be used to combine multiple scoring factors to optimize ranking of documents in web search (i.e., learning to rank), and learn techniques used in recommender systems (also called filtering systems), including content-based recommendation/filtering and collaborative filtering. You will also have a chance to review the entire course.

7 hours to complete

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

10 videos

Lesson 6.1: Learning to Rank - Part 15m

Lesson 6.2: Learning to Rank - Part 210m

Lesson 6.3: Learning to Rank - Part 34m

Lesson 6.4: Future of Web Search13m

Lesson 6.5: Recommender Systems: Content-Based Filtering - Part 112m

Lesson 6.6: Recommender Systems: Content-Based Filtering - Part 210m

Lesson 6.7: Recommender Systems: Collaborative Filtering - Part 16m

Lesson 6.8: Recommender Systems: Collaborative Filtering - Part 212m

Lesson 6.9: Recommender Systems: Collaborative Filtering - Part 34m

Lesson 6.10: Course Summary9m

1 reading

Week 6 Overview10m

2 practice exercises

Week 6 Practice Quiz1h

Week 6 Quiz1h

Reviews

4.5

189 reviews

5 stars
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4 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM TEXT RETRIEVAL AND SEARCH ENGINES

by PESep 16, 2019

This course goes through the basics of text retrieval systems with an appropriate speed. However, the contents are quite out-dated for 2019.

by HJJun 25, 2016

This course is complemented with a software and text (not mandatory) and good explanation with diagrams...

by RRJun 5, 2020

Excellent Course for Computer Science Enthusiastic.

Must and Highly recommend course for all Computer Science and Information technology Aspirant

by THMay 2, 2019

Interesting content

However the prof/instructor should practice not pausing so much when explaining the concepts/contents

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