Foundations of Large Language Models in Python

Large Language Models (LLMs) are transforming how we interact with technology, but understanding how they work under the hood is essential for any modern developer. This text-based course guides you from the absolute basics of natural language processing to working with powerful pre-trained models. You will gain a solid conceptual and practical foundation in LLM architectures, learning how to leverage open-source tools to build, fine-tune, and evaluate models for real-world text generation and processing tasks. What you'll learn: - Understand foundational LLM concepts, transformer architectures, and tokenization techniques. - Load and utilize pre-trained models and datasets using the Hugging Face library. - Fine-tune language models on custom text datasets to adapt them for specific tasks. - Evaluate model performance using industry-standard metrics and identify biases. - Apply basic prompt engineering techniques and explore retrieval-augmented generation (RAG) patterns. The course begins with core terminology and architectural concepts before moving into practical code-based implementations. You will progress through loading models, fine-tuning processes, and modern application patterns, all within a structured written format. This course is designed for beginners with basic Python knowledge who want to enter the field of generative AI. No prior machine learning experience is required. Start your journey into the world of large language models today.