Foundations of AI Engineering

First steps to power of LLMs and Generative AI through basics of transformer architectures, advanced RAG

What you’ll learn
✓ Grasp the Fundamentals of Generative Modeling and Transformer Architectures
✓ Master Prompt Engineering and LLM Interaction
✓ Build Robust Retrieval-Augmented Generation (RAG) Pipelines
✓ Design and Deploy Autonomous AI Agents and Multi-Agent Systems
✓ Code, Pretrain, and Fine-Tune an LLM from Scratch

Requirements
● Absolutely not much! Just need a working laptop.

Description
Disclaimer : This course contains the use of artificial intelligence to provide visuals for better and easy understanding!

The Beginning of an AI Engineer: From Generative Foundations to Autonomous Agents

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the demand for intelligent applications skyrockets, AI engineering—the process of building applications on top of readily available foundation models—has emerged as one of the fastest-growing engineering disciplines in the tech industry.

This beginner friendly course is your definitive guide to mastering this intricate and fast-moving landscape. Designed for Python programmers and software developers with no prior machine learning experience, this course bridges the gap between vision and execution, equipping you to move past generative AI hype and build real-world, production-ready systems.

Course Overview Throughout this comprehensive program, you will transition from understanding the fundamental math of neural networks to orchestrating highly complex, autonomous multi-agent systems. You will learn to skip the boilerplate, move fast, and deploy AI applications with confidence.

What You Will Master

• Build an LLM from Scratch: Demystify the “black box” of artificial intelligence.

• Applied AI Engineering & Prompting: Move beyond the basic chat window. You will master systematic prompt engineering, parameter-efficient fine-tuning (PEFT), and learn how to adapt foundation models to solve specific business problems without needing massive compute resources.

• Production-Ready RAG Pipelines: Overcome LLM hallucinations and memory limitations by building robust Retrieval-Augmented Generation (RAG) systems. Using industry-standard frameworks like LangChain, you will learn to extract, embed, and index private enterprise data, allowing your AI to perform highly accurate semantic searches.

• Autonomous Agents & Tool Integration: Equip your AI with the ability to take real-world actions. You will design autonomous agents capable of independent reasoning, dynamic planning, and invoking external APIs (like calculators, web searches, or databases) to accomplish complex workflows.

• Multi-Agent Orchestration: Scale your solutions by building collaborative AI teams. Using cutting-edge frameworks like AutoGen, CrewAI, and LangGraph, you will orchestrate multi-agent systems where specialized AI personas (e.g., a coder agent and a QA reviewer agent) debate, delegate, and solve ambiguous problems together.

Why Take This Course?

The integration of autonomous agents is changing the nature of work itself, transforming human roles from task executors to strategic task managers. Organizations are actively seeking professionals who can design AI solutions that scale across the enterprise. By combining deep technical knowledge with practical engineering skills, this course offers a clear path to transforming how you build software.

Enroll today to position yourself at the absolute forefront of the AI revolution and become a highly sought-after AI Architect capable of delivering measurable efficiency and a competitive edge to any organization.

Who this course is for
■ Beginners who want to get exposure to generative AI and multi agent systems.

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