Artificial Intelligence A-Z 2026: Build 7 Real-World AI Systems with Agentic AI, Generative AI & Reinforcement Learning

Why This Course Exists

Artificial Intelligence is no longer a research curiosity — it is the defining technology of our era. From self-driving cars to AI agents that browse the web, write code, and make autonomous decisions, the AI landscape in 2026 demands practitioners who understand the full spectrum: Reinforcement Learning that teaches machines to act, Generative AI that creates content and solves problems, and Agentic AI that orchestrates complex multi-step workflows without human intervention.

Most AI courses teach fragments. This one teaches the complete modern AI stack — from foundational neural networks through Q-Learning, Deep Q-Networks, A3C, PPO, SAC, Transformer architectures, LLM fine-tuning with LoRA, and cutting-edge Agentic AI systems — all with hands-on Python projects you build and deploy.

What You Will Build

By the end of this course, you will have built 7 real-world AI systems:

• An AI Lunar Lander using Deep Q-Learning that teaches itself to land a spacecraft
• An AI Pac-Man Player using Deep Convolutional Q-Learning that masters the game from raw pixels
• An AI Walking Robot using A3C that learns complex locomotion from scratch
• A Self-Balancing Agent using PPO with continuous action spaces
• An Autonomous Explorer using SAC for optimal decision-making under uncertainty
• A Fine-Tuned Medical Chatbot using LLaMA with LoRA and knowledge augmentation
• An Agentic AI System with tool use, memory, planning, and multi-agent orchestration

What You Will Learn

• Neural Network Foundations: Understand how artificial neurons, activation functions, backpropagation, and gradient descent power every AI system
• Reinforcement Learning Theory: Master Markov Decision Processes, Bellman equations, Q-values, policies, and the exploration-exploitation tradeoff
• Q-Learning: Build AI agents that learn optimal strategies through trial and error
• Deep Q-Learning (DQN): Combine deep neural networks with RL to solve complex environments
• Deep Convolutional Q-Learning: Train AI agents that learn directly from visual input using CNNs
• A3C (Asynchronous Advantage Actor-Critic): Implement parallel training with actor-critic methods and LSTM memory
• PPO (Proximal Policy Optimization): Master the algorithm behind ChatGPT's RLHF and modern robotics
• SAC (Soft Actor-Critic): Build agents that balance reward maximization with exploration entropy
• Transformer Architecture: Understand attention mechanisms, positional encoding, and the architecture powering GPT, Claude, and Gemini
• LLM Fine-Tuning: Fine-tune large language models with LoRA, QLoRA, PEFT, and Hugging Face Transformers
• Agentic AI: Build autonomous AI agents with tool use, ReAct reasoning, memory systems, and multi-agent collaboration
• Bonus Topics: DDPG for continuous control, World Models, Evolution Strategies, and Genetic Algorithms

Who This Course Is For

• Python developers who want to add AI and machine learning to their skill set
• Data scientists looking to master reinforcement learning and generative AI
• AI enthusiasts who want to understand how modern AI systems actually work
• Computer science students preparing for AI research or industry careers
• Software engineers building AI-powered products and features
• Career switchers targeting the highest-paying field in technology

Prerequisites

• Basic Python programming knowledge (variables, functions, loops, classes)
• High school mathematics (algebra, basic calculus concepts helpful but not required)
• A computer with internet access (Google Colab provides free GPU — no expensive hardware needed)
• Curiosity and willingness to experiment — every chapter includes hands-on coding projects