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Fine-Tuning Embedding Models for Semantic Search

The code provided in this repository is from Marqo's Course on 'Fine-Tuning Embedding Models for Semantic Search'.

About the Course

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!

Who can take this course?

People with beginner-level familiarity with Python and an interest in machine learning. The course is ideal for both beginners and advanced professionals such as machine learning engineers and data scientists.

Why should I take this course?

This course will equip you with cutting-edge skills to create smarter, more intuitive search tools that understand the true meaning behind your queries. Whether you're a complete beginner or a tech enthusiast, you'll gain everything you need to start building powerful, intelligent search solutions.

Course Structure

Questions?

If you have any questions, join our Slack Community and a member of our team will be there to help! If it's course specific, please post your questions in the 'marqo-courses' channel. If it's Marqo specific, please post your question in the 'get-help' channel. You can also send Ellie a private message in the Slack Community if you prefer.