AI fundamentals for Beginners - Learn LLM, Agentic AI, MCP

Artificial Intelligence is no longer just for researchers and data scientists. Generative AI and Large Language Models (LLMs) are becoming part of everyday work — from writing and research to automation and intelligent agents. If you want to actually understand how today's AI systems work (not just use ChatGPT and hope for the best), this course gives you that foundation.

This is a beginner-friendly, hands-on introduction to modern AI — but don't mistake "beginner-friendly" for "surface-level." By the end, you'll have built an AIOPS agent, a Retrieval-Augmented Generation (RAG) pipeline visually using Langflow, and built and deployed your own working MCP (Model Context Protocol) server with real OAuth authentication and API integration — two things most "intro to AI" courses never get close to.

Taught by a cloud architect, not just an AI enthusiast

This course is created by Himanshu Rana, a Cloud Solutions Architect with 16+ years delivering enterprise-grade solutions on AWS and Azure, and a Microsoft Certified Trainer. You're learning AI fundamentals from someone who's also spent a career building production systems — so the explanations are grounded in how this technology actually gets used, not just how it's marketed.

What you'll learn

• Core foundations of Generative AI and how LLMs actually "think" and generate responses

• Tokens, prompts, and how tokenization affects both output quality and API cost

• Prompt engineering techniques to get consistent, reliable results from any LLM

• Retrieval-Augmented Generation (RAG) — the difference between traditional RAG and agentic RAG, and how to build a RAG pipeline visually using Langflow, no heavy coding required

• AI Agents: how they plan, reason, and use tools across vector stores, web search, and APIs

• Model Context Protocol (MCP) — the emerging standard for connecting AI models to real tools and systems, including hosts, clients, servers, and transport layers

• Hands-on project: build your own MCP server to manage Google Calendar, with OAuth authentication and live API access — list, schedule, and delete events through an AI client

• How to run and compare open-source models locally, and build an agentic AI Agent with AWS Bedrock AgentCore

• How you can better use Claude features - Claude Code, Claude Cowork, Claude Skill

Who this course is for

• Beginners with no prior AI or machine learning background who want a real foundation, not just buzzwords

• Developers and non-developers alike who want a visual, low-code way to understand RAG and agentic workflows before diving into code-first frameworks like LangChain or LangGraph

• Product managers, founders, and team leads who need a working mental model of how modern AI systems are built

• Anyone curious about AWS Bedrock Agentcore, MCP, Langflow, or what it actually takes to connect an AI model to a real external tool

A note on scope

This course is intentionally focused — it's the solid on-ramp before you commit to a multi-week, multi-framework bootcamp. You'll walk away with real conceptual depth, a working RAG pipeline built visually in Langflow, and a complete hands-on MCP project — not a half-finished tour through ten different agent frameworks. If you're looking for a clear, practical starting point in AI before specializing into code-heavy frameworks, this is built for exactly that.

By the end of this course, you won't just know about AI — you'll know how it works under the hood, and you'll have built something real with it.