Course Summary
Learn how to build search engines and text retrieval systems with this comprehensive course. Explore various techniques and algorithms for text retrieval and gain hands-on experience with practical projects.Key Learning Points
- Gain an understanding of the basic concepts and techniques of text retrieval
- Learn how to build a search engine and evaluate its performance
- Develop skills in natural language processing and information retrieval
Related Topics for further study
Learning Outcomes
- Build a search engine from scratch
- Apply various techniques and algorithms for text retrieval
- Evaluate the performance of a search engine
Prerequisites or good to have knowledge before taking this course
- Basic programming skills in Python
- Familiarity with data structures and algorithms
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures and quizzes
- Hands-on projects
Similar Courses
- Applied Text Mining in Python
- Text Mining and Analytics
- Natural Language Processing with Probabilistic Models
Related Education Paths
Related Books
Description
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.
Outline
- Orientation
- Course Welcome Video
- Course Introduction Video
- Welcome to Text Retrieval and Search Engines!
- Syllabus
- About the Discussion Forums
- Updating your Profile
- Social Media
- Course Errata
- Orientation Quiz
- Pre-Quiz
- Week 1
- Lesson 1.1: Natural Language Content Analysis
- Lesson 1.2: Text Access
- Lesson 1.3: Text Retrieval Problem
- Lesson 1.4: Overview of Text Retrieval Methods
- Lesson 1.5: Vector Space Model - Basic Idea
- Lesson 1.6: Vector Space Retrieval Model - Simplest Instantiation
- Week 1 Overview
- Week 1 Practice Quiz
- Week 1 Quiz
- Week 2
- Lesson 2.1: Vector Space Model - Improved Instantiation
- Lesson 2.2: TF Transformation
- Lesson 2.3: Doc Length Normalization
- Lesson 2.4: Implementation of TR Systems
- Lesson 2.5: System Implementation - Inverted Index Construction
- Lesson 2.6: System Implementation - Fast Search
- Week 2 Overview
- Week 2 Practice Quiz
- Week 2 Quiz
- Week 3
- Lesson 3.1: Evaluation of TR Systems
- Lesson 3.2: Evaluation of TR Systems - Basic Measures
- Lesson 3.3: Evaluation of TR Systems - Evaluating Ranked Lists - Part 1
- Lesson 3.4: Evaluation of TR Systems - Evaluating Ranked Lists - Part 2
- Lesson 3.5: Evaluation of TR Systems - Multi-Level Judgements
- Lesson 3.6: Evaluation of TR Systems - Practical Issues
- Week 3 Overview
- Programming Assignments Overview
- Week 3 Practice Quiz
- Week 3 Quiz
- Week 4
- Lesson 4.1: Probabilistic Retrieval Model - Basic Idea
- Lesson 4.2: Statistical Language Model
- Lesson 4.3: Query Likelihood Retrieval Function
- Lesson 4.4: Statistical Language Model - Part 1
- Lesson 4.5: Statistical Language Model - Part 2
- Lesson 4.6: Smoothing Methods - Part 1
- Lesson 4.7: Smoothing Methods - Part 2
- Week 4 Overview
- Week 4 Practice Quiz
- Week 4 Quiz
- Week 5
- Lesson 5.1: Feedback in Text Retrieval
- Lesson 5.2: Feedback in Vector Space Model - Rocchio
- Lesson 5.3: Feedback in Text Retrieval - Feedback in LM
- Lesson 5.4: Web Search: Introduction & Web Crawler
- Lesson 5.5: Web Indexing
- Lesson 5.6: Link Analysis - Part 1
- Lesson 5.7: Link Analysis - Part 2
- Lesson 5.8: Link Analysis - Part 3
- Week 5 Overview
- Week 5 Practice Quiz
- Week 5 Quiz
- Week 6
- Lesson 6.1: Learning to Rank - Part 1
- Lesson 6.2: Learning to Rank - Part 2
- Lesson 6.3: Learning to Rank - Part 3
- Lesson 6.4: Future of Web Search
- Lesson 6.5: Recommender Systems: Content-Based Filtering - Part 1
- Lesson 6.6: Recommender Systems: Content-Based Filtering - Part 2
- Lesson 6.7: Recommender Systems: Collaborative Filtering - Part 1
- Lesson 6.8: Recommender Systems: Collaborative Filtering - Part 2
- Lesson 6.9: Recommender Systems: Collaborative Filtering - Part 3
- Lesson 6.10: Course Summary
- Week 6 Overview
- Week 6 Practice Quiz
- Week 6 Quiz
Summary of User Reviews
This course on text retrieval has received positive reviews from its users. Many praised the instructor's expertise and engaging teaching style. Users found the course to be comprehensive and well-structured.Key Aspect Users Liked About This Course
The instructor's expertise and engaging teaching style.Pros from User Reviews
- Comprehensive and well-structured course
- Highly knowledgeable instructor
- Interactive and engaging content
- Useful assignments and quizzes
- Practical applications of the course material
Cons from User Reviews
- Some users found the course to be too technical
- The pace of the course was too fast for some learners
- The course could benefit from more examples and case studies
- Lack of personalized feedback on assignments
- Some users experienced technical difficulties with the platform
ChengXiang Zhai
4.5 Raiting
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