Mongodb Atlas Vector Database: Zero To Advanced With Python
Last updated 7/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.64 GB | Duration: 14h 43m
A hands-on guide to mastering MongoDB Atlas and building Vector Databases with Python, Pymongo and Langchain
What you'll learn
Retrieval-Augmented Generation in MongoDB Atlas
Beginner Commands in MongoDB: Learn basic CRUD operations using MongoDB Shell and Compass.
VectorSearch on the Embedding developed by OpenAI models
Setting Up MongoDB Atlas: Configure and manage a cloud-hosted MongoDB database on MongoDB Atlas
Full text Search, Regular Expression Search on text data
Integration of LangChain with Pymongo
Connecting Python with MongoDB Atlas: Use PyMongo to connect your Python applications to MongoDB Atlas
Advanced CRUD Operations: Perform complex operations like updating multiple documents, using filters, and conditional queries.
Indexing and Aggregation: Learn how to create indexes and build efficient aggregation pipelines to handle large datasets.
Introduction to Vector Databases: Understand vector embeddings and their role in AI applications like similarity search.
Requirements
Knowledge of Python (even beginner-level proficiency is sufficient).
A laptop or desktop with at least 8GB RAM and a stable internet connection.
Familiarity with basic computer operations and interest in databases.
Description
Mastering: MongoDB Atlas Vector Database: Zero to Advanced with PythonThis comprehensive course takes you from MongoDB fundamentals to advanced AI-powered vector databases. Perfect for beginners and enthusiasts wanting to master modern database techniques and AI integration.Course SectionsSection 1: MongoDB Fundamentals Master MongoDB basics using Shell and Compass. Learn database setup, CRUD operations, and core concepts.Section 2: PyMongo & Advanced Queries Dive into Python integration with PyMongo. Build complex queries performed in MongoDB AtlasSection 3: Aggregate Pipeline in Atlas Deep dive into aggregate pipeline and stages like groupby, project, match, conditional statements, switch case and many more.Section 4: Search Techniques Explore text search, regex patterns, and full-text search capabilities within MongoDB.Section 5: MongoDB Atlas & Vector Search Transition to cloud with MongoDB Atlas. Implement vector embeddings for similarity search and semantic applications.Section 6: Introduction to Langchain with OpenAI LLMs there we give you introduction to LangChain OpenAI and how to generate the text and get embeddings using sophisticated OpenAI and API keys.Section 5: RAG Systems Build intelligent Retrieval-Augmented Generation systems combining traditional databases with AI technologies in MongoDB Atlas.Tools & ResourcesTechnologies: MongoDB Shell, Compass, PyMongo, MongoDB Atlas, Vector Search, LangChain, OpenAI EmbeddingsIncluded Materials:Complete Jupyter notebooks with step-by-step codeSample datasets and real-world dataConfiguration files and connection scriptsProject templates and starter codeDocumentation and reference guidesHands-on exercises and solutionsAll code examples, datasets, and resources provided for immediate hands-on practice.
Beginners who want to start their journey in database management and modern data technologies.,Python developers looking to enhance their skills by integrating databases into applications.,AI and Data Enthusiasts interested in vector search and building AI-driven solutions.,Students and professionals aiming to work with scalable cloud databases like MongoDB Atlas.,Small business owners and hobbyists seeking to manage their data efficiently.
Mongodb Atlas Vector Database: Zero To Advanced With Python
