Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and RL — Overview & Key Highlights (2026)
Course introduction
Artificial Intelligence A-Z 2026: Agentic AI, Gen AI, and Reinforcement Learning is a practical, concept-to-application program designed to help learners understand how modern AI systems are built and applied in real-world scenarios. Updated for 2026, the course blends AI theory with hands-on implementation, covering Agentic AI, Generative AI, Large Language Models (LLMs), and Reinforcement Learning (RL).
The course emphasizes building multiple AI systems—from intelligent agents and chatbots to reinforcement learning models—so learners gain both conceptual clarity and applied skills.
Instructor: Multiple instructors-Kirill Eremenko,Hadelin de Ponteves
Last updated: January 2026
Duration: ~15 hours (15h 15m total)
Rating: ★4.4 / 5
Learners: 339,483+
Price (typical sale): ~US$7–12 (varies by Udemy promotions)
Access: Lifetime (mobile + TV)
Certificate: Udemy Certificate of Completion included
Key highlights
- Build 7+ different AI systems for distinct applications
- Covers Agentic AI, Generative AI, and Reinforcement Learning in one course
- Hands-on work with LLMs and Transformers
- Learn modern RL algorithms: Q-Learning, DQN, A3C, PPO, SAC
- Explore LoRA, QLoRA, fine-tuning, and quantization
- Practical NLP techniques for chatbot development
- Apply AI to real-world optimization and decision-making problems
- Balanced mix of theory + implementation
What you will learn
AI Foundations & Theory
- Core Artificial Intelligence concepts explained intuitively
- How modern AI systems are designed and evaluated
- Understanding Agentic AI and autonomous decision-making
Generative AI & Large Language Models
- How LLMs and Transformers work
- Prompting, fine-tuning, and knowledge augmentation
- Low-Rank Adaptation (LoRA) and Quantization (QLoRA)
- Building AI assistants and chatbots
Natural Language Processing (NLP)
- Tokenization, padding, and text preprocessing
- NLP workflows for conversational AI
- Practical chatbot development techniques
Reinforcement Learning (RL)
- Fundamentals of RL environments and agents
- Q-Learning and Deep Q-Learning
- Deep Convolutional Q-Learning
- Advanced methods: A3C, PPO, SAC
- Policy-based vs value-based learning
Advanced & Experimental AI Topics
- Extras like DDPG, world models, and evolution strategies
- Genetic algorithms for optimization problems
- Understanding how advanced RL approaches scale
Real-World Applications
- AI agents powered by cloud-based foundation models
- Optimization use cases such as logistics and process flows
- Applying AI to practical business and engineering problems
Frequently asked questions (FAQs)
Q — Is this course beginner-friendly?
A — It assumes basic Python knowledge and high-school-level math, but AI concepts are explained clearly and progressively.
Q — Does this course focus more on theory or practice?
A — It balances both, combining intuitive explanations with hands-on AI implementations.
Q — Will I learn modern AI methods like LLMs and Agentic AI?
A — Yes. LLMs, transformers, agent-based systems, and generative AI are core topics.
Q — Are reinforcement learning algorithms covered in depth?
A — Yes. Multiple RL algorithms are taught, from fundamentals to advanced approaches.
Q — Is a certificate included?
A — Yes. You receive a Udemy Certificate of Completion.
Why this course is worth it
This course stands out for its breadth and practical focus. It brings together Generative AI, Agentic AI, and Reinforcement Learning—areas often taught separately—into a single, structured learning path. Learners gain hands-on experience building AI systems while also understanding the theory behind them, making the skills transferable to real-world projects.
