Master Vector Database with Python for AI & LLM Use Cases
Learn Vector Database using Python, Pinecone, LangChain, Open AI, Hugging Face and build out AI, ML , Chat applications
Lo que serás capaz de hacer
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!
- 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.
Temario
UNIDAD 1 - Introduction to Vector Database · 5 leccionesUNIDAD 1 - Introduction to Vector Database
UNIDAD 1 - Introduction to Vector Database
- Course Overview
- Introduction to Vector Database
- Why Vector Database
- Vector Database Use Cases
- Quiz on Introduction to Vector Database
UNIDAD 2 - Vector Database Foundations · 8 leccionesUNIDAD 2 - Vector Database Foundations
UNIDAD 2 - Vector Database Foundations
- Section Overview
- Fundamentals of Vector Database
- SQLite Database
- Storing and Retrieving Vector Data in SQLite
- Vector Similarity Search
- Chroma DB-Local Vector Data Base - Part 1: Setup & Data Insertion
- Chroma DB-Local Vector Data Base - Part 2: Query
- Vector database foundation knowledge check
UNIDAD 3 - Pinecone Vector Database Environment Setup · 8 leccionesUNIDAD 3 - Pinecone Vector Database Environment Setup
UNIDAD 3 - Pinecone Vector Database Environment Setup
- Pinecone Account Setup
- Pinecone DB Console Overview
- Setting Up Development Environment in Windows
- Setting Up Development Environment in Ubuntu
- A note on Pinecone upgrade (v-2.2.1 to v-5.0.0)
- "Hello World" Script for Vector DB - [Pinecone v-5.0.0]
- [Older version] "Hello World" Script for Vector DB - [Pinecone v-2.2.1]
- Environment Setup
UNIDAD 4 - Database Operations using Pinecone v5.0.0 [latest as of Oct 2024] · 6 leccionesUNIDAD 4 - Database Operations using Pinecone v5.0.0 [latest as of Oct 2024]
UNIDAD 4 - Database Operations using Pinecone v5.0.0 [latest as of Oct 2024]
- Database Operations: Create, Retrieve, Update and Deletion (CRUD)
- Insert Vectors
- Upsert: Insert and Update [updated for Pinecone v5.0.0]
- Query Vector Data
- Fetch Vectors by ID
- Delete Vector
UNIDAD 5 - [Will be archived] Database Operations using Pinecone v2.2.1 (May 2023) · 6 leccionesUNIDAD 5 - [Will be archived] Database Operations using Pinecone v2.2.1 (May 2023)
UNIDAD 5 - [Will be archived] Database Operations using Pinecone v2.2.1 (May 2023)
- Database Operations: Create, Retrieve, Update and Deletion (CRUD)
- Insert Vectors
- Upsert: Insert and Update
- Query Vector Data
- Fetch Vectors by ID
- Delete Vector
UNIDAD 6 - Data Base Management · 9 leccionesUNIDAD 6 - Data Base Management
UNIDAD 6 - Data Base Management
- Concepts of Index and Collection
- Index Management [pinecone 5.0]
- Partitioning Vectors
- Upsert using Namespace [pinecone v5.0.0]
- Vector Partitioning Using Metadata [v5.0.0]
- Distance Metrics
- Reading Assignment on "Understanding Indexes"
- Quiz on concepts of Pinecone database management
- Index Management [pinecone v2.2.1]
UNIDAD 7 - Project 1: Application in Semantic Search · 7 leccionesUNIDAD 7 - Project 1: Application in Semantic Search
UNIDAD 7 - Project 1: Application in Semantic Search
- Introduction to Semantic Search
- Medium Posts Data Obtaining
- Data Preprocessing
- Preparing for Upsert
- Vector Query: "Semantic Search"
- Quiz on Semantic Search
- Reading Assignment: Read Hugging Face Documents on a sentence transformer
UNIDAD 8 - Project 2: Semantic Search Powered by Named Entity · 11 leccionesUNIDAD 8 - Project 2: Semantic Search Powered by Named Entity
UNIDAD 8 - Project 2: Semantic Search Powered by Named Entity
- Concept of Named Entity Recognition (NER)
- NER Implementation Examples
- Setting up Environment for NER based Semantic Search
- Vector Embedding Models and Load Data
- Data Preparation
- Developing NER Helper Function
- Vector Embedding in Batches
- NER Extraction in Batches
- Metadata Processing
- Vector Upsert
- Vector Query: Semantic Search with NER
UNIDAD 9 - Project 3: Building AI Chat Agent with LangChain and OpenAI · 12 leccionesUNIDAD 9 - Project 3: Building AI Chat Agent with LangChain and OpenAI
UNIDAD 9 - Project 3: Building AI Chat Agent with LangChain and OpenAI
- Building an Retrieval AI Agent with LangChain and OpenAI
- Obtaining OpenAI API
- Data Load
- Vector Embedding Function
- Setup Vector DB
- Processing for Meta Data
- Embedding and OpenAI Rate Limit Workaround
- Indexing
- Semantic Search with OpenAI
- Embedding with OpenAI and LangChain
- Retrieval QA Agent- an example of retrieval augmented generation (RAG)
- Chat Agent
UNIDAD 10 - Project 4: Audio Similarity Search · 9 leccionesUNIDAD 10 - Project 4: Audio Similarity Search
UNIDAD 10 - Project 4: Audio Similarity Search
- Environment Setup
- Loading Audio Data
- Exploring Audio Data
- Embedding Audio Data
- Setting up Vector Database for Audio
- Vector Indexing
- Vector Querying
- Audio Search with your Own Data: Out of sample search
- [older code, will be archived soon] Audio Search out of sample example
UNIDAD 11 - Project 5: Capstone · 1 leccionesUNIDAD 11 - Project 5: Capstone
UNIDAD 11 - Project 5: Capstone
- Build a video search engine based on audio content where "text" is search input.
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
11 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
8 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Dr. KM Mohsin.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 11 unidades · 82 lecciones · 55 h
- 12 meses de acceso
- 8 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Dr. KM Mohsin; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
