<h2>About this course</h2>
<p>Weaviate Academy is your guided introduction to vector databases—a cornerstone technology reshaping how AI systems search, understand, and retrieve information. Created by Weaviate, a leader in vector database innovation, this course teaches you embeddings, semantic search, retrieval-augmented generation (<a href="/topics/rag">RAG</a>), and multi-modal AI in a hands-on, practical way. If you want to move beyond keyword matching and build smarter search experiences, this is where you start.</p>

<h2>What you'll learn</h2>
<ul>
  <li>How embeddings work and why they're the foundation of modern semantic search</li>
  <li>Build and query vector databases to power intelligent recommendation systems</li>
  <li>Implement semantic search—retrieving results based on meaning, not just keywords</li>
  <li>Design and deploy retrieval-augmented generation (RAG) pipelines to connect LLMs with private data</li>
  <li>Work with multi-modal embeddings to search across text, images, and other data types</li>
  <li>Optimize vector database performance for production applications</li>
  <li>Integrate Weaviate into Python and other application frameworks</li>
</ul>

<h2>Who this is for</h2>
<p>You're ready for this course if you've touched Python or basic machine learning concepts and want to understand the AI infrastructure that powers next-generation search and recommendation systems. No vector database experience needed.</p>
<ul>
  <li>AI Engineers — master vector databases to build semantic search and RAG systems that directly impact product capabilities</li>
  <li>Backend <a href="/for/developers">Developers</a> — learn the infrastructure behind intelligent search, enabling you to architect data retrieval pipelines for modern applications</li>
  <li><a href="/for/data-scientists">Data Scientists</a> — bridge the gap between model training and production, discovering how embeddings and vector databases unlock real-world AI value</li>
</ul>

<h2>Prerequisites</h2>
<p>Comfortable with Python basics (variables, functions, libraries). Familiarity with how APIs work is helpful. Some exposure to machine learning concepts (even conceptual) will smooth the learning curve, but isn't required.</p>

<h2>Why this matters for Indian learners</h2>
<p>Vector databases and semantic search are moving from research labs into mainstream production—and companies across India are hiring for these skills right now. E-commerce platforms in India (including major players like Flipkart and Amazon India) are building smarter search and recommendation engines; fintech and healthtech startups in Bangalore and Mumbai are embedding RAG systems into customer-facing products; and AI teams at global tech giants with India hubs are deepening their vector infrastructure. These skills directly translate to higher-paying engineering roles and faster promotions in India's growing AI job market.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. Weaviate Academy is completely free—no hidden charges, no paid tier for course material. You learn the same content everyone else does.</p>
<h3>How long will it take to complete?</h3>
<p>The course is designed for 10 hours of learning. That's roughly 2–3 hours per week if you spread it over a month, or you can move faster depending on your pace and how much you practice with hands-on projects.</p>
<h3>Will I get a certificate?</h3>
<p>Yes. On completion, you'll receive a certificate from Weaviate Academy that you can add to your resume and LinkedIn profile.</p>

Weaviate

Weaviate's free vector database academy — embeddings, semantic search, RAG, multi-modal.

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