This hands-on course teaches how to build a Large Language Model (LLM) completely from scratch using practical coding steps. It is designed for learners who want to deeply understand how models like GPT are constructed, trained, and optimized for real-world tasks.
The course begins with setting up a proper development environment and preparing text data for machine learning workflows. You will learn how raw text is processed and structured for training AI models.
Next, the course introduces the attention mechanism, which is the core component of transformer-based models. You will then implement a GPT-style model from scratch and use it to generate text, helping you understand how modern AI systems produce language.
The training phase covers pretraining on unlabeled data, where the model learns patterns in language at scale. After that, you will explore fine-tuning techniques, including classification tasks and instruction-based fine-tuning to improve model performance and usability.
By the end of the course, you will have built a full LLM pipeline from scratch, including data preparation, attention mechanisms, GPT implementation, pretraining, and fine-tuning. This gives you a strong practical and theoretical foundation in modern AI model development.
