Scrimba
Learn Embeddings and Vector Databases
Scrimba

Learn Embeddings and Vector Databases

This course is part of AI Engineering Specialization

Guil Hernandez

Instructor: Guil Hernandez

2,076 already enrolled

Included withCoursera Plus

Gain insight into a topic and learn the fundamentals.
4.6

(15 reviews)

Intermediate level
Some related experience required
2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.6

(15 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

Details to know

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Assessments

1 assignment

Taught in English

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Build your subject-matter expertise

This course is part of the AI Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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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

Instructor

Guil Hernandez
Scrimba
7 Courses16,896 learners

Offered by

Scrimba

Recommended if you're interested in Machine Learning

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4.6

15 reviews

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VS
5

Reviewed on Mar 21, 2024

MM
5

Reviewed on Oct 11, 2024

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