Getting Started with Vector Databases and AI Embeddings.

Format:

Video

Language:

English

Physical Description:

1 online resource (120 minutes)

Place of Publication:

[Place of publication not identified] : PACKT Publishing, 2025.

Language Note:

In English.

System Details:

video file

Summary:

<b>Learn vectors embeddings and vector databases to power AI tools from semantic search to recommendation engines with a step by step beginner friendly journey into data driven AI.</b><h4>Key Features</h4><ul><li>Beginner focused foundation of vectors embeddings and databases in AI </li><li>Real world relevance with use cases from search RAG and recommendations </li><li>Clear pedagogy combining theory context and applied practical examples </li></ul><h4>Book Description</h4>Artificial intelligence relies on vectors and embeddings to make sense of information in ways traditional databases cannot. This course begins by exploring the foundations, starting with the concept of vectors, embeddings, and similarity metrics. You’ll see how raw data is transformed into meaningful numerical representations that AI can analyze and compare. From there, you move into vector databases, the specialized systems designed to manage structured and unstructured data at scale. You’ll learn the workflows of vector search, understand how these databases function, and explore how to choose the right one for your projects. The course provides context for both the theory and the practical steps of working with vectorized information. The final section focuses on industry use cases that highlight the transformative impact of vector databases. You’ll discover how they power semantic search, drive recommendation engines, enhance retrieval-augmented generation, and support anomaly detection and visual search. By the end of the course, you’ll be prepared to apply these concepts to build innovative AI-driven solutions.<h4>What you will learn</h4><ul><li>Understand how vectors represent unstructured information</li><li>Explore embeddings and their role in semantic search tasks</li><li>Learn similarity metrics for comparing vectorized data</li><li>Apply vector databases to manage structured and raw data</li><li>Discover workflows for efficient vector search operations</li><li>Implement AI use cases from RAG to recommendation systems</li></ul><h4>Who this book is for</h4>This course is designed for beginners curious about AI and data, as well as professionals seeking practical knowledge of embeddings and vector databases. It is ideal for data scientists, software developers, engineers, and business analysts who want to explore semantic search and AI applications. Product managers and tech leaders will also benefit from understanding how these technologies shape modern solutions. No advanced AI or database skills are required, though basic AI familiarity is useful.

Notes:

Title from resource description page (viewed July 20, 2026).