Descriptions
Mastering Vector Databases & Embedding Models in 2025, Master the core principles and practical implementations of vector databases and embedding models in this comprehensive, hands-on course. You will start by understanding how text and multimodal data are transformed into high-dimensional numerical vectors using state-of-the-art transformer architectures. The curriculum covers key vector similarity metrics such as cosine similarity, dot product, and Euclidean distance, while guiding you through creating and visualizing embeddings, building mini-search engines, and fine-tuning domain-specific embedding models with contrastive loss.
As you advance, you will explore the inner workings of vector database indexing and retrieval algorithms, including Hierarchical Navigable Small World (HNSW) graphs and Inverted File (IVF) indexing. Through practical coding exercises in FAISS and Pinecone, you will learn to construct scalable vector indexes, execute fast similarity searches, and build real-world AI applications such as Retrieval-Augmented Generation (RAG) pipelines, semantic search engines, and personalized recommender systems. By the end of this course, you will possess the practical expertise to select, optimize, and deploy vector databases for production-grade AI solutions.
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.
Who this course is for
- Data scientists, ML engineers, and software developers exploring vector search.
- AI practitioners and enthusiasts who want to apply embeddings in real-world projects.
- Professionals interested in semantic search, RAG, and recommender systems.
Specificatoin of Mastering Vector Databases & Embedding Models in 2025
- Publisher : Udemy
- Teacher : Tensor Teach
- Language : English
- Level : Intermediate
- Number of Course : 22
- Duration : 1 hours and 58 minutes
Content of Mastering Vector Databases & Embedding Models in 2025
Requirements
- Basic Python knowledge recommended, but step-by-step coding lessons are provided.
- No prior experience with embeddings, vector databases, or similarity search is required.
Pictures
Sample Clip
Installation Guide
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Subtitle : English
Quality: 1080p
Download Links
Password file(s): www.downloadly.ir
File size
706 MB