This course offers an advanced journey into the realm of AI engineering, focusing on the creation, utilization, and management of embeddings in vector databases. Learners will begin by grasping the concept of embeddings and their pivotal role in AI's interpretative processes. The course progresses through practical exercises on setting up environment variables, creating embeddings, and integrating these into vector databases with tools like Supabase.
Learn Embeddings and Vector Databases
Learn Embeddings and Vector Databases
This course is part of AI Engineering Specialization
Instructor: Guil Hernandez
2,179 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.6
(16 reviews)
Intermediate level
Some related experience required
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Understand and Create Embedding
Utilize Vector Databases
Implement Advanced AI Solutions
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
1 assignment
Taught in English
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This course is part of the AI Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There is 1 module in this course
This course provides an in-depth look at AI engineering with a focus on creating and using embeddings in vector databases. Starting with the basics of embeddings, learners will advance through practical tasks involving environment setup, embedding creation, and database integration using tools like Supabase. Challenges will test skills in text pairing, semantic and similarity searches, and crafting AI conversational responses, leading to a final project that solidifies their understanding. This course stands out for its mix of theory and hands-on practice, preparing participants to develop an AI chatbot by the end.
What's included
1 assignment19 plugins
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Recommended if you're interested in Machine Learning
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University of Michigan
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