Gain a basic understanding of how numerical representations transform language! Explore the world of text embeddings in this online course, covering essential topics such as tokenization, historical models, modern techniques, and practical applications.
It's free of charge and no prior AI experience is necessary.
Language: English
Beginner, Big Data and AI, Data Science
Course information
In Natural Language Processing, embeddings are the keys that open the door for algorithms to process and understand the meaning of words or text, facilitating more effective language-based tasks such as sentiment analysis, language translation, and document similarity.
In this course we can help you to understand better what embeddings are, what they are useful for, and how they have developed over time. We will also talk about how to store and work with embeddings by using vector databases.
As a complement to this course, the KI-Servicezentrum Berlin Brandenburg (KISZ-BB) offers regularly a presential hands-on workshop that applies in a practical way all the knowledge from this course, using the example of semantic search. If you would like to participate in the workshop, you can check our events or subscribe to our newsletter and get regular information about workshops and other educational offers.
What you'll learn
- AI
- Embeddings
- Natural Language Processing
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Learners
Certificate Requirements
- Gain a Confirmation of Participation by completing at least 50% of the course material.
Find out more in the certificate guidelines.
This course is offered by
Mario Tormo Romero is an AI Engineer / Senior Data Scientist with a Master's degree in Physics and Mathematics, with over 30 years of programming experience. He studied at the Universidad de Valencia (Estudi General), Spain, and the Freie Universität Berlin, Germany, and has been working in the field of Data Science and AI for the past 5 years, on various roles such as Data Scientist, AI Engineer, MLOps Engineer, and Technical Project Manager. He has worked in diverse industries, including Healthcare, Real Estate and Social Media.