Master Vector Database with Python for AI & LLM Use Cases

Master Vector Database with Python for AI & LLM Use Cases, available at $79.99, has an average rating of 4.41, with 79 lectures, 5 quizzes, based on 703 reviews, and has 5646 subscribers.

You will learn about Pinecone Vector Database, LangChain, Transformer Models for vector embedding, Generative AI, Open AI API Usage, Hugging Face Models Master the essential techniques for vector data embedding, indexing, and retrieval. A Practical Code Along with Semantic Search Use Case in Detail with Named Entity Recognition Developing an AI Chat Bot for Cognitive Search on Private Data Using LangChain Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models). Explore various vector database technologies, including Pinecone, and learn how to set up and configure a vector database environment. Learn how vector databases enhance AI workflows by enabling efficient similarity search and nearest neighbor retrieval. Gain practical knowledge on integrating vector databases with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn. Implement code along exercises to build and optimize vector indexing systems for real-world applications. Explore practical use cases of vector databases in AI, generative AI, and LLM, such as recommendation systems, content generation, and language translation. Understand how vector databases can handle large-scale datasets and support real-time inference. Gain insights into performance optimization techniques, scalability considerations, and best practices for vector database implementation. This course is ideal for individuals who are Data engineers, database administrators and data professionals curious about the emerging field of vector databases. or Data scientists and analysts interested in exploring advanced AI techniques. or Machine learning engineers seeking to enhance their knowledge of vector databases and their applications. or AI researchers and practitioners looking to leverage vector databases for generative AI models. or Software developers interested in integrating vector databases into their applications. or Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area. or Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML. It is particularly useful for Data engineers, database administrators and data professionals curious about the emerging field of vector databases. or Data scientists and analysts interested in exploring advanced AI techniques. or Machine learning engineers seeking to enhance their knowledge of vector databases and their applications. or AI researchers and practitioners looking to leverage vector databases for generative AI models. or Software developers interested in integrating vector databases into their applications. or Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area. or Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML.

Enroll now: Master Vector Database with Python for AI & LLM Use Cases

Summary

Title: Master Vector Database with Python for AI & LLM Use Cases

Price: $79.99

Average Rating: 4.41

Number of Lectures: 79

Number of Quizzes: 5

Number of Published Lectures: 64

Number of Published Quizzes: 5

Number of Curriculum Items: 86

Number of Published Curriculum Objects: 71

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Pinecone Vector Database, LangChain, Transformer Models for vector embedding, Generative AI, Open AI API Usage, Hugging Face Models
Master the essential techniques for vector data embedding, indexing, and retrieval.
A Practical Code Along with Semantic Search Use Case in Detail with Named Entity Recognition
Developing an AI Chat Bot for Cognitive Search on Private Data Using LangChain
Understand the fundamentals of vector databases and their role in AI, generative AI, and LLM (Language Model Models).
Explore various vector database technologies, including Pinecone, and learn how to set up and configure a vector database environment.
Learn how vector databases enhance AI workflows by enabling efficient similarity search and nearest neighbor retrieval.
Gain practical knowledge on integrating vector databases with Python, utilizing popular libraries like NumPy, Pandas, and scikit-learn.
Implement code along exercises to build and optimize vector indexing systems for real-world applications.
Explore practical use cases of vector databases in AI, generative AI, and LLM, such as recommendation systems, content generation, and language translation.
Understand how vector databases can handle large-scale datasets and support real-time inference.
Gain insights into performance optimization techniques, scalability considerations, and best practices for vector database implementation.

Who Should Attend

Data engineers, database administrators and data professionals curious about the emerging field of vector databases.
Data scientists and analysts interested in exploring advanced AI techniques.
Machine learning engineers seeking to enhance their knowledge of vector databases and their applications.
AI researchers and practitioners looking to leverage vector databases for generative AI models.
Software developers interested in integrating vector databases into their applications.
Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area.
Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML.

Target Audiences

Data engineers, database administrators and data professionals curious about the emerging field of vector databases.
Data scientists and analysts interested in exploring advanced AI techniques.
Machine learning engineers seeking to enhance their knowledge of vector databases and their applications.
AI researchers and practitioners looking to leverage vector databases for generative AI models.
Software developers interested in integrating vector databases into their applications.
Students and academics studying AI, machine learning, or data science who want to expand their knowledge in this specialized area.
Individuals with a technical background or a strong interest in AI and databases, eager to explore cutting-edge technologies shaping the future of AI and ML.

In this comprehensive course on Vector Databases, you will delve into the exciting world of cutting-edge technologies that are transforming the field of artificial intelligence (AI), particularly in generative AI. With a focus on Future-Proofing Generative AI, this course will equip you with the knowledge and skills to harness the power of Vector Databases for advanced applications, including Language Model Models (LLM), Generative Pretrained Transformers (GPT) like ChatGPT, and Artificial General Intelligence (AGI) development.

Starting from the foundations, you will learn the fundamentals of Vector Databases and their role in revolutionizing AI workflows. Through practical examples and hands-on coding exercises, you will explore techniques such as vector data indexing, storage, retrieval, and conditionality reduction. You will also gain proficiency in integrating Pinecone Vector Data Base with other tools like LangChain, OpenAI API using Python to implement real-world use cases and unleash the full potential of Vector Databases.

Throughout the course, we will uncover the limitless possibilities of Vector Databases in generative AI. You will discover how these databases enable content generation, recommendation systems, language translation, and more. Additionally, we will discuss performance optimization, scalability considerations, and best practices for efficient implementation.

Led by an expert instructor with a PhD in computational nano science and extensive experience as a data scientist at leading companies, you will benefit from their deep knowledge, practical insights, and passion for teaching AI and Machine Learning (ML). Join us now to embark on this transformative learning journey and position yourself at the forefront of Future-Proofing Generative AI with Vector Databases. Enroll today and unlock a world of AI innovation!

Course Curriculum

Chapter 1: Introduction to Vector Database

Lecture 1: Course Overview

Lecture 2: Introduction to Vector Database

Lecture 3: Why Vector Database

Lecture 4: Vector Database Use Cases

Chapter 2: Vector Database Foundations

Lecture 1: Section Overview

Lecture 2: Fundamentals of Vector Database

Lecture 3: SQLite Database

Lecture 4: Storing and Retrieving Vector Data in SQLite

Lecture 5: Vector Similarity Search

Lecture 6: Chroma DB-Local Vector Data Base – Part 1: Setup & Data Insertion

Lecture 7: Chroma DB-Local Vector Data Base – Part 2: Query

Chapter 3: Pinecone Vector Database Environment Setup

Lecture 1: Pinecone Account Setup

Lecture 2: Pinecone DB Console Overview

Lecture 3: Setting Up Development Environment in Windows

Lecture 4: Setting Up Development Environment in Ubuntu

Lecture 5: "Hello World" Script for Vector DB

Chapter 4: Database Operations

Lecture 1: Database Operations: Create, Retrieve, Update and Deletion (CRUD)

Lecture 2: Insert Data

Lecture 3: Upsert: Insert and Update

Lecture 4: Query Vector Data

Lecture 5: Fetch Vectors by ID

Lecture 6: Delete Vector

Chapter 5: Data Base Management

Lecture 1: Concepts of Index and Collection

Lecture 2: Index Management

Lecture 3: Partitioning Vectors

Lecture 4: Upsert using Namespace

Lecture 5: Vector Partitioning Using Metadata

Lecture 6: Distance Metrics

Chapter 6: Project 1: Application in Semantic Search

Lecture 1: Introduction to Semantic Search

Lecture 2: Medium Posts Data Obtaining

Lecture 3: Data Preprocessing

Lecture 4: Preparing for Upsert

Lecture 5: Vector Query: "Semantic Search"

Chapter 7: Project 2: Semantic Search Powered by Named Entity

Lecture 1: Concept of Named Entity Recognition (NER)

Lecture 2: NER Implementation Examples

Lecture 3: Setting up Environment for NER based Semantic Search

Lecture 4: Vector Embedding Models and Load Data

Lecture 5: Data Preparation

Lecture 6: Developing NER Helper Function

Lecture 7: Vector Embedding in Batches

Lecture 8: NER Extraction in Batches

Lecture 9: Metadata Processing

Lecture 10: Vector Upsert

Lecture 11: Vector Query: Semantic Search with NER

Chapter 8: Project 3: Building AI Chat Agent with LangChain and OpenAI

Lecture 1: Building an Retrieval AI Agent with LangChain and OpenAI

Lecture 2: Obtaining OpenAI API

Lecture 3: Data Load

Lecture 4: Vector Embedding Function

Lecture 5: Setup Vector DB

Lecture 6: Processing for Meta Data

Lecture 7: Embedding and OpenAI Rate Limit Workaround

Lecture 8: Indexing

Lecture 9: Semantic Search with OpenAI

Lecture 10: Embedding with OpenAI and LangChain

Lecture 11: Retrieval QA Agent- an example of retrieval augmented generation (RAG)

Lecture 12: Chat Agent

Chapter 9: Project 4: Audio Similarity Search

Lecture 1: Environment Setup

Lecture 2: Loading Audio Data

Lecture 3: Exploring Audio Data

Lecture 4: Embedding Audio Data

Lecture 5: Setting up Vector Database for Audio

Lecture 6: Vector Indexing

Lecture 7: Vector Querying

Lecture 8: Audio Search with your Own Data: Out of sample search

Chapter 10: Project 5: Capstone

Instructors

Dr. KM Mohsin
Data Scientist, Computational Scientist: Nano-Electronics

Rating Distribution

1 stars: 13 votes
2 stars: 19 votes
3 stars: 88 votes
4 stars: 249 votes
5 stars: 340 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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