Course overview
Embeddings transform text into numerical vectors that capture semantic meaning, enabling AI systems to understand context beyond simple keyword matching. In this course, you’ll learn how to generate embeddings using both open-source models and APIs, visualize them in reduced dimensions, measure similarity between vectors, and build semantic search systems that power modern AI applications.
Key skills
- Generating embeddings from text using both local and API-based models
- Reducing and visualizing high-dimensional vector spaces using PCA
- Implementing and comparing different similarity measurement approaches
- Building production-ready semantic search functions with caching and optimization
- Applying embeddings to real-world AI systems including search and RAG
Course outline
Understanding Embeddings [3 lessons]
Understanding, Generating, and Visualizing Embeddings 2h
Lesson Objectives- Convert text into numerical vectors capturing semantic meaning
- Understand embeddings cluster similar content mathematically together
- Reduce high-dimensional embeddings using PCA for visualization
- Generate embeddings using sentence-transformers and pre-trained models
- Apply embeddings to search, RAG, and AI systems
Generating Embeddings with APIs and Open Models 2h
Lesson Objectives- Collect research papers programmatically using arXiv API
- Generate embeddings using both local and cloud-based models
- Compare self-hosted versus API embedding service tradeoffs
- Visualize high-dimensional embeddings using PCA dimensionality reduction
- Store and manage embeddings for production applications
Measuring Similarity and Distance between Embeddings 2h
Lesson Objectives- Implement three similarity metrics: Euclidean, dot product, cosine
- Build a complete semantic search function with embeddings
- Visualize and interpret search results in embedding space
- Evaluate search quality using scores and qualitative analysis
- Apply production optimizations: caching, batching, vector databases
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