Vector Databases Fundamentals [NEW]
Vector Databases Fundamentals [NEW], available at $54.99, has an average rating of 4.7, with 55 lectures, based on 49 reviews, and has 581 subscribers.
You will learn about Understand Vector Databases Explore Top Solutions Build Vector Databases Master Advanced Querying This course is ideal for individuals who are Data Scientists and Analysts or Developers and Engineers or AI Enthusiasts and Researchers or Beginners in Data Management It is particularly useful for Data Scientists and Analysts or Developers and Engineers or AI Enthusiasts and Researchers or Beginners in Data Management.
Enroll now: Vector Databases Fundamentals [NEW]
Summary
Title: Vector Databases Fundamentals [NEW]
Price: $54.99
Average Rating: 4.7
Number of Lectures: 55
Number of Published Lectures: 55
Number of Curriculum Items: 55
Number of Published Curriculum Objects: 55
Original Price: $129.99
Quality Status: approved
Status: Live
What You Will Learn
- Understand Vector Databases
- Explore Top Solutions
- Build Vector Databases
- Master Advanced Querying
Who Should Attend
- Data Scientists and Analysts
- Developers and Engineers
- AI Enthusiasts and Researchers
- Beginners in Data Management
Target Audiences
- Data Scientists and Analysts
- Developers and Engineers
- AI Enthusiasts and Researchers
- Beginners in Data Management
In the era of big data and AI, managing and extracting meaningful insights from vast amounts of unstructured data is more crucial than ever. “Mastering Vector Databases: From Foundations to Advanced Applications” is your comprehensive guide to understanding, building, and leveraging vector databases to transform your data management capabilities.
What You Will Learn:
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Foundations of Vector Databases: This course will help you gain a solid understanding of vector databases, why they are essential, and how they differ from traditional databases.
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Overview of Top Vector Database Solutions: Explore the top 5 vector database solutions, including Pinecone and Chroma, and understand their unique features and key differences.
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Building Vector Databases from Scratch: Learn how to construct your vector database from the ground up, focusing on metrics, data structures, and efficient data storage.
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Vectorization Techniques: Master converting unstructured data into meaningful vectors using abstraction frameworks and embedding techniques.
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Hands-On Projects: Apply your knowledge with practical projects that demonstrate real-world applications of vector databases, including AI-driven search, document clustering, and personalized content recommendations.
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Advanced Querying and Retrieval: Understand how to quickly perform efficient similarity searches and retrieve relevant data using advanced querying techniques.
Why Enroll in This Course?
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Comprehensive Curriculum: This course covers both foundational knowledge and advanced applications, ensuring a complete understanding of vector databases.
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Expert Instruction: Learn from an experienced instructor who provides clear, step-by-step guidance throughout the course.
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Hands-On Learning: Use practical projects and real-world examples to solidify your understanding and apply what you’ve learned.
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Cutting-Edge Tools: Get hands-on experience with leading vector databases solutions like Pinecone and Chroma, and learn how to integrate them with OpenAI for powerful data management capabilities.
Who Should Enroll?
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Data Scientists and Analysts looking to enhance their data management skills.
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Developers and Engineers interested in building robust, scalable data solutions.
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AI Enthusiasts and Researchers aiming to leverage vector databases for advanced applications.
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Anyone interested in mastering the latest tools and techniques for managing unstructured data.
Transform your data into actionable insights with the power of vector databases. Enroll now and start your journey toward mastering vector databases today!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction – Course prerequisites and structure
Chapter 2: Source Code and Resources
Lecture 1: Source code and resources
Chapter 3: Vector Databases Deep Dive – Fundamentals
Lecture 1: Introduction to Vector Databases – Full Overview
Lecture 2: Why Vector Databases
Lecture 3: Vector Databases – Benefits and Advantages
Chapter 4: Traditional vs Vector Databases – Differences
Lecture 1: Traditional vs Vector Databases – Limitations and Challenges
Lecture 2: Vector Databases & Embeddings – Full Work Flow
Lecture 3: Embeddings vs Vectors – Differences
Lecture 4: Vector Databases – How They Work and Advantages
Lecture 5: Vector Databases Use Cases
Lecture 6: Vector and Traditional Databases – Summary
Chapter 5: Vector Databases Solutions – Top 5 Vector Databases
Lecture 1: The Top 5 Vector Databases – Overview
Lecture 2: Understanding LLM (Large Language Models)
Lecture 3: Check in
Chapter 6: Building Vector Databases – Hands-on – Chroma Vector Database
Lecture 1: Development Environment Setup
Lecture 2: Setup VS-Code, Python and OpenAI API Key
Lecture 3: Chroma Database workflow
Lecture 4: Creating a Chroma Vector Database & Adding Documents & Querying them
Lecture 5: Looping Through the Results & Showing Similarity Search Results
Lecture 6: Chroma Default Embedding Function
Lecture 7: Chroma Vector Database – Persisting Data and Saving
Lecture 8: Creating an OpenAI Embeddings – Raw without Chroma
Lecture 9: Using OpenAIs Embedding API to Create Embedding in Chroma
Lecture 10: Vector Databases Metrics and Data Structures
Lecture 11: Section Summary
Chapter 7: Common Measures of Vector Similarity
Lecture 1: Vector Similarity Deep Dive – Cosine Similarity
Lecture 2: Euclidean Distance – L2 Norm
Lecture 3: Dot Product
Lecture 4: Section Summary
Chapter 8: Vector Databases and LLM – the Full Workflow
Lecture 1: Vector Databases and LLM – Deep Dive
Lecture 2: Loading all Documents
Lecture 3: Generating Embeddings from Documents & Insert then into Chroma Database
Lecture 4: Getting the Relevant Chunks when Given a Query
Lecture 5: Using OpenAI LLM to Generate Response – Full Flow
Lecture 6: Section Summary
Chapter 9: Vector Databases & the Langchain Framework
Lecture 1: The LangChain Framework – Quick Overview
Lecture 2: Getting started with LangChain and the OpenAIChat Wrapper
Lecture 3: Loading Documents with LangChain Document Loader
Lecture 4: Splitting the Documents with LangChain
Lecture 5: Creating a Chroma Vector Database with LangChain
Lecture 6: Getting the Response from the Model – the Complete WorkFlow
Chapter 10: Pinecone Vector Database
Lecture 1: Pinecone – Deep Dive
Lecture 2: Create Pinecone Account & Dashboard Overview
Lecture 3: Creating our Pinecone Index in Code
Lecture 4: Upserting and Querying our Pinecone Index
Lecture 5: Querying Pinecone Manually in the Dashboard
Lecture 6: Using LangChain Pinecone Wrapper – Create Index and Upsert & Similarity Search
Lecture 7: Creating a Retriever and Chain Objects & a LLM to get a Response
Lecture 8: Clean up – Delete Pinecone Index
Lecture 9: Challenge – Explore other Vector Database
Lecture 10: Section Summary
Chapter 11: Choosing the Right Vector Database
Lecture 1: Choosing the Right Vector Database – Comparison Tables
Lecture 2: Which Database Should I Choose?
Lecture 3: Choosing the Right Database – Criterias
Chapter 12: Wrap up & Next Steps
Lecture 1: Congratulations and Next Steps
Instructors
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Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor
Android, Flutter, AWS, Best Selling Instructor
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 3 votes
- 4 stars: 16 votes
- 5 stars: 30 votes
Frequently Asked Questions
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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