"Grasp Vector DB Basics" is an intermediate course for machine learning practitioners and data professionals looking to understand the technology powering modern semantic search and AI applications. In an era where keyword search is no longer enough, this course builds a strong conceptual foundation, explaining how vector databases store and retrieve vector representations, how similarity-based retrieval differs from traditional database querying, and how these capabilities enable applications such as semantic search and recommendation.

Grasp Vector DB Basics

Grasp Vector DB Basics
This course is part of Vector DB Foundations, Embeddings & Search Algorithms Specialization

Instructor: LearningMate
Access provided by Innovecs
Recommended experience
What you'll learn
Explain vector databases, analyze use cases to select the best DB solution, and justify your architectural choice to stakeholders.
Skills you'll gain
Tools you'll learn
Details to know

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March 2026
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There are 3 modules in this course
In this module, you will discover the fundamental concepts that make modern AI search possible. You will learn what a vector database is, how it uses embeddings to understand unstructured data, and why this enables a "semantic search" that goes far beyond simple keywords.
What's included
1 video1 reading2 assignments
In this module, you will learn the critical skills of architectural analysis and stakeholder communication. You will compare vector databases to their relational and NoSQL counterparts, and then apply this knowledge by building a decision matrix and a final stakeholder pitch.
What's included
2 videos1 reading2 assignments
In this final module, you will learn how to translate your technical analysis into a persuasive business case. You will focus on structuring a compelling argument to convince stakeholders to adopt your recommended database solution.
What's included
1 video1 reading2 assignments
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