This advanced freeCodeCamp course teaches how to build a complete Large Language Model (LLM) from scratch using pure PyTorch. It is designed for learners who want a deep, practical understanding of how modern AI systems are constructed, trained, and aligned.

The course starts with the core Transformer architecture, explaining how attention mechanisms and neural network layers form the foundation of LLMs. You will then learn how to train a small language model step by step, giving you hands-on experience with real model development.

Next, the course explores modern improvements to transformer architectures and how to scale models effectively for better performance. It also covers advanced concepts such as Mixture of Experts (MoE), which improves efficiency and model capacity.

A key part of the course focuses on fine-tuning techniques, including Supervised Fine-Tuning (SFT), where models are trained on curated datasets to improve behavior and accuracy.

The final sections introduce advanced alignment methods, including Reward Modeling and Reinforcement Learning from Human Feedback (RLHF) using PPO. These techniques are essential for making AI systems safer, more useful, and aligned with human intent.

By the end of this course, learners will have built a full LLM pipeline from scratch, including training, scaling, fine-tuning, and alignment, giving them strong practical and theoretical expertise in modern AI engineering.