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Full Stack AI Masterclass: 18 Courses in 1
Rating: 4.8 out of 5(6 ratings)
3,773 students

Full Stack AI Masterclass: 18 Courses in 1

Build full stack AI apps using LLMs, embeddings, vector search, APIs, and modern deployment workflows
Created byCreative Online School
Last updated 6/2026
English

What you'll learn

  • Learn to store, index, and query vector embeddings in MongoDB for intelligent AI-driven search.
  • Build full-stack AI apps using LLMs, vector search, and real-time data pipelines with MongoDB Atlas
  • Implement RAG workflows to boost LLM accuracy, reduce hallucinations, and deliver context-aware results.
  • Deploy scalable, production-ready AI features with indexing, performance tuning, and secure APIs.

Course content

16 sections • 98 lectures • 5h 0m total length

  • High-Level Architecture of a Modern AI App1:10
  • How Modern AI Applications Actually Work0:01
  • Quiz on: How Modern AI Applications Actually Work
  • Vector Databases vs Traditional Databases0:51
  • Vector Databases vs Traditional Databases0:01
  • Quiz on: Vector Databases vs Traditional Databases
  • Traditional Search vs Semantic Search0:01
  • Quiz on : Traditional Search vs Semantic Search
  • MongoDB vs Traditional SQL Databases for AI1:26
  • Why MongoDB for AI? In Depth Analysis0:02

Requirements

  • Students should have basic computer skills, beginner-level programming knowledge, and a willingness to learn MongoDB and AI concepts.

Description

This Full Stack AI Masterclass is a practical, end-to-end guide to building intelligent, production-ready applications powered by Large Language Models (LLMs) and vector search. The course is designed for developers who want to go beyond theory and actually build real AI-driven systems used in modern products.

You will learn how to design, develop, and deploy full stack AI applications that combine frontend interfaces, backend APIs, LLM intelligence, and vector-based retrieval systems. Instead of focusing on one tool or vendor, this course teaches core concepts and architectures that work across modern AI stacks.

We start by breaking down how LLM-powered applications work internally, including embeddings, semantic search, and retrieval-augmented generation (RAG). You will then implement vector search to store and query high-dimensional data, enabling features like AI chat, document search, recommendation systems, and contextual question answering.

On the backend side, you will build scalable APIs that connect LLMs with vector databases and business logic. You will learn how to structure AI pipelines, manage prompts, handle context efficiently, and optimize performance for real-world usage. Authentication, error handling, and production best practices are also covered.

On the frontend, you will create interactive user experiences such as chat interfaces and AI-powered dashboards that communicate with your backend services. This ensures you truly understand the full stack workflow, not just isolated AI components.

Finally, the course focuses on deployment and scalability, showing you how to prepare AI applications for real users. You will learn how to structure projects for maintainability, handle costs, and make intelligent design decisions that scale.

By the end of this course, you will be able to architect, build, and deploy full stack AI applications using LLMs and vector search with confidence. Whether you are a backend developer, frontend developer, or aspiring AI engineer, this masterclass equips you with job-ready, future-proof AI skills.

Who this course is for:

  • For beginners, developers, and AI learners who want to build real-world intelligent apps using MongoDB and LLMs.
  • Ideal for students and developers eager to integrate vector search and AI features into modern applications

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