This comprehensive course teaches how to build Large Language Models (LLMs) from scratch, with a strong focus on understanding how systems like GPT actually work under the hood. It is designed for learners who want to move beyond using AI tools and instead understand how they are built and trained.
The course starts with an introduction to LLM fundamentals, including the difference between pretraining and fine-tuning, and how transformer-based models power modern AI systems. You will learn how GPT models operate, how they process language, and how they generate responses.
A major part of the course focuses on building core components from scratch, including a tokenizer in Python and understanding Byte Pair Encoding (BPE), which is essential for text processing in LLMs. You will also learn how input-output datasets are created using DataLoaders for training language models.
The course explains embeddings, positional encodings, and the full data preprocessing pipeline used in LLM development. It also introduces the attention mechanism, which is the core innovation behind transformer architectures.
By the end of this course, learners will have a deep understanding of how LLMs are constructed step by step, including tokenization, embeddings, transformers, and training workflows, giving them strong foundations in advanced AI engineering.
