University of Illinois at Urbana-Champaign

Text Retrieval and Search Engines

This course is part of Data Mining Specialization

Taught in English

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ChengXiang Zhai

Instructor: ChengXiang Zhai

57,791 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(942 reviews)

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90%

30 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

14 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(942 reviews)

|

90%

30 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Mining Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 7 modules in this course

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.

What's included

2 videos6 readings2 quizzes1 plugin

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.

What's included

6 videos1 reading2 quizzes

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.

What's included

6 videos1 reading2 quizzes

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.

What's included

6 videos2 readings2 quizzes1 programming assignment

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.

What's included

7 videos1 reading2 quizzes

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.

What's included

8 videos1 reading2 quizzes

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.

What's included

10 videos1 reading2 quizzes1 programming assignment1 plugin

Instructor

Instructor ratings
4.4 (84 ratings)
ChengXiang Zhai
University of Illinois at Urbana-Champaign
4 Courses102,022 learners

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