Text Retrieval and Search Engines
Master the science behind search engines, from text analysis to recommender systems. Perfect for data mining and information retrieval.
Master the science behind search engines, from text analysis to recommender systems. Perfect for data mining and information retrieval.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Mining Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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English
پښتو, বাংলা, اردو, 3 more
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What you'll learn
Master text retrieval and search engine fundamentals
Implement vector space and probabilistic retrieval models
Design and evaluate information retrieval systems
Understand web crawling and indexing techniques
Apply machine learning for document ranking
Develop recommendation and filtering systems
Skills you'll gain
This course includes:
7.6 Hours PreRecorded video
14 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 7 modules in this course
This comprehensive course covers the fundamental concepts and technologies behind modern search engines and text retrieval systems. Students learn about natural language processing, vector space models, probabilistic retrieval models, and web search technologies. The curriculum includes practical implementation aspects like inverted indexing, ranking algorithms, and evaluation methods. Advanced topics cover web crawling, link analysis, machine learning for ranking, and recommender systems, providing a complete foundation in search engine technology.
Orientation
Module 1 · 2 Hours to complete
Week 1
Module 2 · 3 Hours to complete
Week 2
Module 3 · 3 Hours to complete
Week 3
Module 4 · 6 Hours to complete
Week 4
Module 5 · 3 Hours to complete
Week 5
Module 6 · 3 Hours to complete
Week 6
Module 7 · 6 Hours to complete
Fee Structure
Instructor
Pioneer in Information Retrieval and Text Mining Research
Dr. ChengXiang Zhai serves as Donald Biggar Willett Professor in Engineering at the University of Illinois Urbana-Champaign's Department of Computer Science, with joint appointments at the Institute for Genomic Biology, Department of Statistics, and School of Information Sciences. His groundbreaking research in information retrieval and text mining has earned him numerous prestigious honors, including the ACM SIGIR Gerard Salton Award (2021), ACM Fellowship (2017), and the Presidential Early Career Award for Scientists and Engineers (PECASE). After earning his initial degrees from Nanjing University and a PhD from Carnegie Mellon University, he has published over 200 papers with an H-index of 58, significantly advancing the fields of natural language processing, machine learning, and bioinformatics. His work has received multiple ACM SIGIR Test of Time Awards, reflecting his lasting impact on the field. Through his courses "Text Mining and Analytics," "Text Retrieval and Search Engines," and "Data Mining Project" on Coursera, he continues to shape the next generation of computer scientists while maintaining editorial roles with major journals and conference leadership positions in his field
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Frequently asked questions
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