Data Strategy and Feature Engineering for Semantic Search

Traditional keyword search often fails to capture the true intent behind user queries. Building an effective semantic search system requires a solid data foundation and smart feature engineering to bridge the gap between human language and machine understanding. This text-only course guides you through the process of establishing a robust search data strategy. You will understand how to transform raw text into rich vector embeddings, extract meaningful relevance signals, and prepare high-quality training data for machine learning search models. What you'll learn: - Understand the foundational concepts of semantic search, vector embeddings, and dual-encoder architectures. - Design a structured data strategy to capture and utilize user behavior and relevance signals. - Apply query preprocessing techniques to clean, normalize, and enrich search inputs. - Engineer features from text data to train machine learning models for ranking and retrieval. - Prepare high-quality training datasets for dual encoders and modern bi-encoder models. - Explore modern vector database integration and basic retrieval-augmented generation patterns. The course begins with essential terminology and the mechanics of vector search, gradually advancing to practical feature engineering workflows and data preparation strategies. You will learn through clear written explanations, conceptual breakdowns, and step-by-step code snippets. This course is designed for software engineers, data analysts, and aspiring machine learning practitioners who want to understand the data side of search. No prior machine learning experience is required, though basic familiarity with Python is helpful. Start reading today to master the data strategies powering modern search engines.