Artificial Intelligence A-Z™: Learn How To Build An AI

Course snapshot

Agentic AI
Generative AI
Reinforcement Learning
Q-Learning
Deep Q-Learning (DQN)
Convolutional Deep Q-Learning
A3C (Asynchronous Advantage Actor-Critic)
PPO (Proximal Policy Optimization)
SAC (Soft Actor-Critic)
Transformers
Large Language Models (LLMs)
LoRA & QLoRA fine-tuning
Text classification & extraction
Prompt design for LLMs
Knowledge augmentation for LLMs

This 2025 edition of Artificial Intelligence A-Z blends agentic AI, generative AI, and reinforcement learning into a single practical track. You start with hands-on prompting basics, how to shape factual answers, summaries, extractions, classifications, conversations, code, and reasoning, and learn to evaluate responses with objective and subjective metrics and parameter tuning. From there you build a suite of reinforcement learning systems: classic Q Learning, Deep Q Learning, and a convolutional DQN for games like Pac Man and control tasks like a lunar lander. You then move to policy gradient families including A3C, PPO, and SAC for more robust decision making, including self driving simulations. On the generative side, you work with transformers and large language models, applying LoRA and QLoRA to fine tune efficiently and using knowledge augmentation to specialize an LLM, for example a medical chatbot. The course code is built in Google Colab to avoid setup pain, with downloadable .py and .ipynb templates so you can remix quickly. Along the way, intuition first explanations keep the math approachable while still giving you a correct mental model of how each algorithm learns. Extras include DDPG, world models, and evolution strategies and genetic algorithms, plus a bonus mini course on generative AI and cloud. By the end you have multiple working AIs, agentic LLM apps and RL agents, plus a toolkit for evaluating prompts, tuning models, and turning ideas into runnable systems.

Perfect forBeginners and practitioners who want hands-on AI builds, Developers learning RL algorithms through projects, Builders experimenting with LLMs, agentic patterns, and fine-tuning

Not forLearners seeking heavy theory-first math treatments, Those wanting platform-specific certificates from universities

Price0.00–0.00

Pricing and availability can change. Always check the provider page.

Build practical AIs—agents, LLM apps, and reinforcement learners—by coding them end-to-end.— Course team on Udemy

Syllabus

Course Overview
Introduction to Prompting & Real-World Examples
Creating Effective Prompts (factual, summarization, extraction, classification, conversation, code, reasoning)
Evaluating Prompt Performance (objective/subjective metrics, parameter tuning)
Advanced Prompt Techniques (zero/few-shot, chain-of-thought, least-to-most, generated knowledge)
RL Foundations: Q-Learning, Deep Q-Learning, Conv DQN
RL Advanced: A3C, PPO, SAC
LLMs & GenAI: Transformers, LoRA/QLoRA, fine-tuning with knowledge augmentation
Projects: Chatbot with a foundation model; warehouse optimization; lunar lander; Pac-Man; Kung Fu; self-driving car; medical chatbot
Extras: DDPG, World Models, Evolution Strategies & Genetic Algorithms