Vector Databases Deep Dive

Free Download Vector Databases Deep Dive
Published 12/2023
MP4 | Video: h264, 1920×1080 | 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.
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