In this comprehensive tutorial, you will learn how to build a Large Language Model (LLM) from scratch using Python. The course begins with an introduction to the basics of natural language processing (NLP) and deep learning, explaining key concepts like tokenization, embeddings, attention mechanisms, and transformers. You will gain hands-on experience by implementing each component step by step, starting from data preprocessing to building the neural network architecture.

The tutorial walks you through creating a tokenizer to convert text into numerical representations, designing a transformer-based model, and training it on sample datasets. You will also learn how to optimize your model, prevent overfitting, and evaluate performance using standard metrics. Finally, the course covers deployment strategies, allowing you to run your model efficiently for generating text or performing other NLP tasks. By the end of this tutorial, you will have a solid understanding of the inner workings of LLMs and the practical skills to experiment and build your own AI-powered language models using Python.