AI A-Z [2026]: Agentic AI, Gen AI, Prompt Engineering and RL

About this course

Welcome to Artificial Intelligence A-Z!

This course is structured in 10 parts:

Part 1 - Prompt Engineering: Prompt Engineering & Prompt Templates, Prompt Engineering Techniques, The 4 Elements of a (good) prompt, Inference Parameters

Part 2 - Generative AI: Fundamentals of Generative AI, Image Generation, Foundation Models Overview, Foundation Models Lifecycle, Data Selection, Foundation Models Selection, Training vs. Inference, Context Window, Tokens and Embeddings, Transformers, Foundation Models Training, Foundation Models Fine-Tuning, Foundation Models Evaluation, Retrieval-Augmented Generation (RAG) for Cooking Assistance Part 3 - Agentic AI: AI Agents, Building a Cloud-powered AI Agent for Business Assistance

Part 4 - Fundamentals of Reinforcement Learning: Q-Learning Intuition, Q-Learning Implementation

Part 5 - Deep Q-Learning: Deep Q-Learning Intuition, Deep Q-Learning Implementation for Moon LandingPart 6 - Deep Convolutional Q-Learning: Deep Convolutional Q-Learning Intuition, Deep Convolutional Q-Learning Implementation for Pac-ManPart 7 - A3C: A3C Intuition, A3C Implementation for Kung FuPart 8 - PPO and SAC: Proximal Policy Optimization, Soft Actor-Critic, Build and Train the PPO & SAC models for Self-Driving CarsPart 9 - LLMs: The Ingredients of an LLM, Who invented LLMs, How LLMs generate text, Understand what's inside an LLM, The LLM Parameters, The LLM Context Window, How to Fine-Tune LLMs for Medical Assistance

Part 10 - Responsible AI: Features of Responsible AI, Guardrails in Generative AI, Legal Risks of Generative AI, AWS Tools for Responsible AI, A

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