Hadelin de Ponteves – Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL (Download)
Artificial intelligence is reshaping industries—from autonomous vehicles to intelligent chatbots and optimized business processes. Yet many aspiring AI developers struggle with dense theory, complex mathematics, and installation headaches that prevent them from actually building working AI systems. The gap between understanding AI concepts and deploying practical solutions stops countless learners from entering this high-demand field. Learning AI through hands-on projects that combine cutting-edge techniques like Agentic AI, Generative AI, and Reinforcement Learning bridges this gap effectively.
Course Overview
Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL teaches you to build seven different AI applications from scratch using state-of-the-art models. Created by Hadelin de Ponteves, Kirill Eremenko, and the SuperDataScience Team—instructors known for making complex topics accessible—this course prioritizes intuition over overwhelming mathematics. You’ll code alongside instructors in Google Colab, eliminating setup frustrations while creating practical AI solutions for real-world problems.
What You’ll Learn
Agentic AI
Build a cloud-powered AI agent for business assistance using Foundation Models (LLMs), creating intelligent systems that can autonomously perform tasks and make decisions.
Reinforcement Learning Fundamentals
Master core RL concepts including Q-Learning, Bellman Equations, Markov Decision Processes, Temporal Difference learning, and action selection strategies. Understand how AI agents learn optimal policies through trial and error.
Deep Q-Learning
Train an AI to land on the moon using neural networks combined with Q-Learning. Learn experience replay, epsilon-greedy strategies, and how deep learning enhances traditional reinforcement learning approaches.
Deep Convolutional Q-Learning
Build an AI that plays Pac-Man by processing visual game environments through convolutional neural networks, demonstrating how AI learns from pixel-level input.
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Advanced RL Algorithms
Implement A3C (Asynchronous Advantage Actor-Critic) for fighting games, PPO (Proximal Policy Optimization) for self-driving cars, and SAC (Soft Actor-Critic) for autonomous vehicle control—mastering the latest reinforcement learning techniques.
Generative AI and LLMs
Fine-tune Llama 2 using Hugging Face, LoRA, and QLoRA quantization techniques. Build an AI Doctor Chatbot that understands medical terminology through knowledge augmentation and NLP techniques like tokenization and padding.
Bonus Content
Receive three additional AI implementations (DDPG, Full World Model, Evolution Strategies) and a free 3-hour Generative AI course upon completion.
Course Structure
Each module combines intuition lectures explaining the “why” behind techniques with step-by-step coding tutorials starting from blank pages. Build complete projects including warehouse optimization, lunar landing, game playing, autonomous driving, and medical chatbots using downloadable Python templates.
Who Should Enroll
This course welcomes anyone interested in AI, machine learning, or deep learning, regardless of experience level.
