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Fine-Tuning 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!

Introduction

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!

Let's dive right in!

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