This Training & Fine-Tuning LLMs course is a structured and practical guide to understanding how large language models are trained and adapted for real-world applications. It is designed for learners who want to move beyond theory and understand the full pipeline of LLM development.
You will begin with an introduction to LLM training, learning how large-scale models are built from data and how training processes shape their behavior. The course explains the difference between pretraining and fine-tuning and why both are essential in modern AI systems.
Next, you will study data preparation for LLMs. This includes how datasets are collected, cleaned, formatted, and optimized for training. High-quality data is a key factor in building effective and reliable language models.
The course also covers evaluation methods for LLMs, teaching you how to measure model performance, detect weaknesses, and improve output quality using structured testing approaches.
In the final section, you will explore advanced fine-tuning techniques. These methods show how models can be adapted for specific tasks such as summarization, question answering, and conversational AI systems.
By the end of this course, you will have a complete understanding of the LLM lifecycle, from data preparation and training to evaluation and fine-tuning.
This course is ideal for AI engi
