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There are 7 modules in this course
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.
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 assignments1 plugin
Show info about module content
2 videos•Total 15 minutes
Course Welcome Video•3 minutes
Course Introduction Video•12 minutes
6 readings•Total 60 minutes
Welcome to Text Retrieval and Search Engines!•10 minutes
Syllabus•10 minutes
About the Discussion Forums•10 minutes
Updating your Profile•10 minutes
Social Media•10 minutes
Course Errata•10 minutes
2 assignments•Total 45 minutes
Pre-Quiz•30 minutes
Orientation Quiz•15 minutes
1 plugin•Total 15 minutes
Welcome! Please tell us about yourself.•15 minutes
Week 1
4 hours to complete
Module details
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 assignments
Show info about module content
6 videos•Total 94 minutes
Lesson 1.1: Natural Language Content Analysis•21 minutes
Lesson 1.2: Text Access•9 minutes
Lesson 1.3: Text Retrieval Problem•26 minutes
Lesson 1.4: Overview of Text Retrieval Methods•10 minutes
Lesson 1.5: Vector Space Model - Basic Idea•10 minutes
Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation•18 minutes
1 reading•Total 10 minutes
Week 1 Overview•10 minutes
2 assignments•Total 120 minutes
Week 1 Practice Quiz•60 minutes
Week 1 Quiz•60 minutes
Week 2
4 hours to complete
Module details
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 assignments
Show info about module content
6 videos•Total 102 minutes
Lesson 2.1: Vector Space Model - Improved Instantiation•17 minutes
Lesson 2.2: TF Transformation•10 minutes
Lesson 2.3: Doc Length Normalization•19 minutes
Lesson 2.4: Implementation of TR Systems•21 minutes
Lesson 2.5: System Implementation - Inverted Index Construction•18 minutes
Lesson 2.6: System Implementation - Fast Search•17 minutes
1 reading•Total 10 minutes
Week 2 Overview•10 minutes
2 assignments•Total 120 minutes
Week 2 Practice Quiz•60 minutes
Week 2 Quiz•60 minutes
Week 3
7 hours to complete
Module details
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.
Lesson 3.2: Evaluation of TR Systems - Basic Measures•13 minutes
Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 1•16 minutes
Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 2•10 minutes
Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements•11 minutes
Lesson 3.6: Evaluation of TR Systems - Practical Issues•15 minutes
2 readings•Total 20 minutes
Week 3 Overview•10 minutes
Programming Assignments Overview•10 minutes
2 assignments•Total 120 minutes
Week 3 Practice Quiz•60 minutes
Week 3 Quiz•60 minutes
1 programming assignment•Total 180 minutes
Programming Assignment 1•180 minutes
Week 4
4 hours to complete
Module details
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 assignments
Show info about module content
7 videos•Total 88 minutes
Lesson 4.1: Probabilistic Retrieval Model - Basic Idea•13 minutes
Lesson 4.4: Statistical Language Model - Part 1•12 minutes
Lesson 4.5: Statistical Language Model - Part 2•10 minutes
Lesson 4.6: Smoothing Methods - Part 1•10 minutes
Lesson 4.7: Smoothing Methods - Part 2•13 minutes
1 reading•Total 10 minutes
Week 4 Overview•10 minutes
2 assignments•Total 120 minutes
Week 4 Practice Quiz•60 minutes
Week 4 Quiz•60 minutes
Week 5
4 hours to complete
Module details
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 assignments
Show info about module content
8 videos•Total 99 minutes
Lesson 5.1: Feedback in Text Retrieval•7 minutes
Lesson 5.2: Feedback in Vector Space Model - Rocchio•12 minutes
Lesson 5.3: Feedback in Text Retrieval - Feedback in LM•19 minutes
Lesson 5.4: Web Search: Introduction & Web Crawler•11 minutes
Lesson 5.5: Web Indexing•17 minutes
Lesson 5.6: Link Analysis - Part 1•9 minutes
Lesson 5.7: Link Analysis - Part 2•18 minutes
Lesson 5.8: Link Analysis - Part 3•6 minutes
1 reading•Total 10 minutes
Week 5 Overview•10 minutes
2 assignments•Total 120 minutes
Week 5 Practice Quiz•60 minutes
Week 5 Quiz•60 minutes
Week 6
7 hours to complete
Module details
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.
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Reviewed on May 18, 2020
A bit difficult to complete as the Quiz questions were tougher. But when you go through all, you might feel good.
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Reviewed on Aug 23, 2018
I'd appreciate Prof.Zhai's lesson design with robust framework. BUT I really can not accept that the mistake that homework putted in wrong week and STILL NOT FIXED AFTER TWO YEARS!
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Reviewed on Jun 5, 2020
Excellent Course for Computer Science Enthusiastic.Must and Highly recommend course for all Computer Science and Information technology Aspirant
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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.
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.