Vector Databases Deep Dive

Mastering Vector Databases: Fundamental Concepts to Advanced Applications in AI and Big Data

What you'll learn

Understand the Principles and Mechanics of Vector Databases

Proficiency in Implementing Various Indexing Strategies

Apply Vector Databases in Real-world Scenarios

Explore Advanced Concepts and Future Trends

Requirements

Before enrolling in this course on vector databases, participants should have a foundational understanding of general database concepts, including the basics of data storage, retrieval, and management, as well as a grasp of both traditional relational (SQL) and non-relational (NoSQL) databases. A basic knowledge of data structures and algorithms is important, as the course will delve into indexing methods and search algorithms.

Proficiency in python programming is essential for understanding the implementation aspects of vector databases and data manipulation.

A basic understanding of machine learning concepts, particularly data representation and feature extraction, will be beneficial. Experience with data analysis and visualization tools, such as Jupyter Notebooks and Pandas, is also recommended for practical exercises within the course.

Description

This in-depth course on vector databases is tailored for data professionals who aspire to master the intricacies of modern database technologies. It begins with a fundamental understanding of vector databases, including their structure, operation, and various types like Pinecone, Qdrant, Milvus, and Weaviate. Participants will learn to navigate through different indexing strategies such as Flat Index, Inverted File Index, ANNOY, Product Quantization, and Hierarchical Navigable Small World, understanding which method suits specific data scenarios.The course delves into practical applications, teaching learners how to apply vector databases in real-world settings such as recommendation systems and anomaly detection. It covers advanced topics like Federated Learning, Graph Embeddings, Real-time Vector Search, and BI Connectivity, ensuring learners are prepared for future advancements in the field.A significant part of the course is dedicated to real-world case studies, allowing participants to apply theoretical knowledge to practical scenarios. This includes exploring how these databases integrate with AI and machine learning, enhancing data analysis, and decision-making processes across various industries.Ideal for data engineers, AI researchers, and analysts, the course demands a basic understanding of database concepts, data structures, algorithms, and machine learning principles. Participants should also be comfortable with programming, especially in Python.Upon completion, learners will have a comprehensive understanding of vector databases, equipped with the skills to implement them effectively in their professional endeavors.

Overview

Section 1: Introduction

Lecture 1 Introduction to the Course

Lecture 2 Course Structure

Section 2: Introduction to Vector Databases

Lecture 3 Introduction to Vector Databases

Lecture 4 Key Principles of Vector Databases

Lecture 5 Why are Vector Databases all the rage

Lecture 6 How Vector Databases Differ from Traditional Databases

Lecture 7 Advantages & Challenges of Vector Databases

Section 3: Vector Database Core Concepts

Lecture 8 Introduction to Vectors

Lecture 9 Real World Illustration on Vectors

Lecture 10 Vectors and their roles in databases

Lecture 11 Introduction to Embeddings

Lecture 12 Embeddings Illustrations - Fraud Detection Example

Lecture 13 Introduction to Dimensionality and High-Dimension Spaces

Lecture 14 Challenges with High-Dimensional Data

Lecture 15 Distance Metrics and Similarity

Lecture 16 Euclidean Distance

Lecture 17 Manhattan Distance

Lecture 18 Cosine Distance

Lecture 19 Jaccard Similarity

Section 4: Understanding Search Similariity

Lecture 20 The Importance of Search Similarity

Lecture 21 K-Nearest Neighbors

Lecture 22 Approximate Nearest Neighbors

Lecture 23 KNN vs. ANN

Section 5: Indexing and Querying

Lecture 24 Indexing Strategies

Lecture 25 Flat Index

Lecture 26 Flat Index Imagined - Real World Illustration

Lecture 27 Inverted File Index

Lecture 28 Inverted File Index Imagined - Real World Illustration

Lecture 29 Approximate Nearest Neighbors Oh Yeah - ANNOY

Lecture 30 ANNOY Imagined - Real World Illustration

Lecture 31 Product Quantization

Lecture 32 Product Quantization Imagined - Real World Illustration

Lecture 33 Hierarchical Navigable Small World (HNSW)

Lecture 34 HNSW Imagined - Real World Illustration

Lecture 35 Selecting the right index

Section 6: Working with Vector Databases

Lecture 36 Vector Database or Vector Store

Lecture 37 Pinecone

Lecture 38 Qdrant

Lecture 39 Milvus

Lecture 40 Weaviate

Section 7: Demo

Lecture 41 Pinecone Demo

Section 8: The Future of Vector Daabases

Lecture 43 The Future of Vector Databases

This course on vector databases is ideally suited for data professionals who are looking to deepen their understanding and skills in advanced database technologies. It will particularly benefit data scientists, data engineers, and machine learning practitioners who have a foundational grasp of database concepts and are proficient in programming language. The course is also valuable for analysts and AI enthusiasts who are keen on exploring how vector databases can enhance data analysis, especially those who have a basic understanding of machine learning principles.,It is perfect for professionals who are comfortable with data structures and algorithms and are eager to learn about sophisticated indexing methods and real-time data processing. This course will also appeal to those interested in the practical applications of these databases in fields like healthcare, finance, and e-commerce, and who are open to engaging with complex theoretical concepts and their practical applications in the evolving landscape of big data and AI.