Mastering Vector Databases & Embedding Models
Learn embeddings, similarity search, HNSW, IVF, semantic search, RAG, and recommender systems with hands-on examples.
★★★★★ 4.4
(8 reviews)
2 hours of content
What you'll learn
- Explain what embeddings are and how they enable similarity search.
- Learn how to choose and fine-tune embedding models for custom applications.
- Learn how vector databases work in terms of indexing & retrieval.
- Familiarize yourself with the vector database landscape and different applications.
Explains embeddings and similarity search, covering indexing and retrieval in vector databases, including HNSW and IVF methods. Learners compare embedding models and explore applications such as semantic search, retrieval-augmented generation, and recommender systems. The course includes hands-on examples for choosing and fine-tuning models.
Who this course is for
Ideal for machine learning practitioners and developers who want to apply vector databases and embeddings to search and RAG applications.
#embeddings
#vector-databases
#rag
#similarity-search
#hnsw
#semantic-search