Building Search Engines and Text Retrieval Systems

With the explosive growth of digital text—from web pages and articles to social media posts—the ability to find relevant information quickly is more critical than ever. Understanding how search engines process, index, and retrieve this massive volume of natural language is the key to building modern, data-driven applications. In this course, you will transition from understanding basic text processing to designing functional search and retrieval systems. You will learn the mechanics behind search engines, how to rank documents by relevance, and how to implement modern search techniques that go beyond simple keyword matching. What you'll learn: - Understand the fundamental concepts of text processing, tokenization, and normalization - Build inverted indexes to enable fast and efficient document lookup - Apply classic retrieval models like TF-IDF and BM25 to score and rank search results - Evaluate search engine performance using standard metrics like precision and recall - Explore modern semantic search concepts, including vector embeddings and vector database basics - Implement basic query expansion and feedback techniques to improve retrieval accuracy The course begins with foundational text-processing concepts before guiding you through indexing, ranking algorithms, and modern semantic search paradigms. You will progress through structured text explanations and practical code snippets designed to reinforce your understanding. This course is designed for beginners interested in data science, software development, or information retrieval, with no prior search engine experience required. Start reading today to unlock the power of text retrieval and build smarter search systems.