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    Vector Databases Deep Dive

    Posted By: ELK1nG
    Vector Databases Deep Dive

    Vector Databases Deep Dive
    Published 12/2023
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 651.03 MB | Duration: 1h 47m

    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.