Information Retrieval System
This subtitle uses the keyword "Information Retrieval" and highlights four core areas covered in your course: Search Al
Information Retrieval System udemy course free download
This subtitle uses the keyword "Information Retrieval" and highlights four core areas covered in your course: Search Al
This course provides a comprehensive introduction to Information Retrieval (IR) Systems, which are at the core of search engines, digital libraries, recommendation platforms, and many AI applications. Students will explore the techniques and algorithms that allow machines to process, index, and retrieve relevant information from large collections of unstructured data.
Key topics include document representation, indexing, Boolean and vector space models, ranking algorithms, web search, evaluation metrics, relevance feedback, query expansion, and the role of natural language processing (NLP) in retrieval systems.
Through hands-on exercises, case studies, and mini-projects, students will gain both theoretical knowledge and practical experience in building and evaluating IR systems.
Learning Outcomes:
Understand the architecture and components of modern IR systems
Apply indexing and retrieval models to textual data
Evaluate IR performance using standard metrics like precision, recall, and MAP
Explore advanced topics such as web crawling, link analysis, and personalized search
Gain exposure to tools and techniques used in real-world IR applications
This course provides a comprehensive introduction to Information Retrieval (IR) Systems, which are at the core of search engines, digital libraries, recommendation platforms, and many AI applications. Students will explore the techniques and algorithms that allow machines to process, index, and retrieve relevant information from large collections of unstructured data.
Key topics include document representation, indexing, Boolean and vector space models, ranking algorithms, web search, evaluation metrics, relevance feedback, query expansion, and the role of natural language processing (NLP) in retrieval systems.
Through hands-on exercises, case studies, and mini-projects, students will gain both theoretical knowledge and practical experience in building and evaluating IR systems.
Learning Outcomes:
Understand the architecture and components of modern IR systems
Apply indexing and retrieval models to textual data
Evaluate IR performance using standard metrics like precision, recall, and MAP
Explore advanced topics such as web crawling, link analysis, and personalized search
Gain exposure to tools and techniques used in real-world IR applications
