Fine-Tune Embedding Models for Semantic Search
Master the art of teaching machines to understand human language with our free course! Dive into vector representations and embeddings, explore sentence and vision transformers, and learn to fine-tune embedding models for different applications. Plus much more!
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In this course, you will learn how Natural Language Processing (NLP) powers semantic search and its real-world applications. Such applications include search engines, virtual assistants and recommendation systems as implemented by leading technology companies like Google, Amazon and Netflix. From vector search fundamentals to fine-tuning embedding models, you will gain a comprehensive understanding of modern NLP and semantic search techniques, regardless of your background.
In addition to mastering the basics of vector search, this course will also show you how to fine-tune embedding models. Learning to fine-tune these models allows you to customize them for different uses, making them more effective in real-life situations. By the end of the course, you'll not only understand the theory but also gain practical experience applying these techniques to solve everyday NLP problems!
Introduction to Vector Embeddings
Learn the foundations of vector representations and how machines encode meaning into high-dimensional space. Understand how embeddings power modern search and recommendation systems.
Sentence Transformers & NLP
Explore sentence transformers and how they capture semantic meaning from natural language. See how leading companies like Google and Amazon apply these techniques at scale.
Vision Transformers for Image Search
Dive into vision transformers and multi-modal embeddings that bridge text and image understanding for powerful cross-modal search applications.
Fine-Tuning Embedding Models
Learn how to fine-tune pre-trained embedding models for your specific domain. Customize models for better relevance in real-life search and recommendation scenarios.
Semantic Search in Practice
Apply everything you have learned to build end-to-end semantic search pipelines. Explore practical implementations using Marqo and open-source tooling.
Evaluating & Improving Search Quality
Understand how to measure retrieval performance and iteratively improve search quality using evaluation metrics, human feedback, and automated testing.
New modules coming soon!
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