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Description of  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 Say "Thank You"