Generative AI Full Course (2026) | Complete Beginner to Advanced Course

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Want to learn Generative AI from scratch and understand how ChatGPT, Gemini, Claude, DeepSeek, RAG, AI Agents, Prompt Engineering, and Transformers actually work?

This complete Generative AI course is designed for beginners, developers, product managers, business leaders, and AI enthusiasts who want practical knowledge of modern AI systems.

In this course, you'll learn:

AI vs Machine Learning vs Deep Learning vs Generative AI
How ChatGPT, Gemini & Claude work behind the scenes
Tokens, Embeddings & Context Windows
Transformer Architecture Explained Simply
Self-Attention Mechanism (Q, K, V)
RNN vs Transformer
AI Hallucinations & How to Fix Them
Prompt Engineering Mastery
Temperature, Top-K & Top-P Controls
Fine-Tuning vs PEFT
Context Engineering & RAG (Retrieval Augmented Generation)
AI Agents & Multi-Agent Systems
LangChain & AI Application Development
Mixture of Experts (MoE) Architecture
DeepSeek R1 & Reasoning Models
Building Enterprise-Ready AI Applications

Whether you're preparing for AI jobs, building AI products, creating AI startups, or simply trying to understand the future of technology, this course gives you a complete roadmap from Zero to Scale.

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⏱ TIMESTAMPS
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00:00 Introduction & Course Overview
00:22 Chapter 1: AI vs ML vs Deep Learning vs Generative AI
02:56 Chapter 2: Why Generative AI is Different
06:27 Chapter 3: What happens when you send a prompt through chatgpt
12:24 Chapter 4: AI Hallucinations Explained
15:00 Chapter 5: Types of LLM Architectures (GPT, BERT, T5)
19:51 Chapter 6: Why Transformers Beat RNNs
24:44 Chapter 7: Transformer Architecture Explained
27:30 Chapter 8: Self-Attention Mechanism
31:05 Chapter 9: Feed Forward Networks (FFN)
33:45 Chapter 10: Reading LLM Configuration Files
36:01 Chapter 11: Mixture of Experts (MoE)
37:50 Chapter 12: Understanding Reasoning Models
40:38 Chapter 13: Control LLM Output: Temperature, Top-K & Top-P Explained
44:39 Chapter 14: Prompt Engineering Mastery
48:38 Chapter 15: Context Engineering
51:00 Chapter 16: The 4-Step AI Application Framework
54:38 Chapter 17: Model Selection: Build vs. Buy and SLM vs. LLM
58:35 Chapter 18: Model Selection: Model Compression and Efficiency
01:02:31 Chapter 19: Fine-Tuning and PEFT
01:05:38 Chapter 20: RAG: Integrating AI apps with External systems
01:09:26 Chapter 21: Model Alignment and Safety
01:11:58 Chapter 22: Evaluations
01:16:14 Chapter 23: What is Agentic AI
01:20:57 Chapter 24: Agentic AI Design patterns
01:24:50 Chapter 25: Model Context Protocol (MCP)
01:27:50 Chapter 26: Orchestration: Using AI Agents Framework
01:32:02 Chapter 27: AI agents Evals and Deployment
01:36:33 Final Thoughts
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