Fine-Tuning Techniques for AI Models

Pretrained AI models provide an excellent starting point, but real-world AI applications often require them to be adapted for specific tasks and domains. In this course, you will learn the practical techniques used to prepare high-quality datasets, fine-tune large language models, optimize training workflows, and evaluate model performance using industry-standard practices. You will begin by exploring data preparation techniques, including data cleaning, tokenization, dataset splitting, label engineering, and task formulation. Next, you will learn supervised fine-tuning concepts such as training dynamics, learning rate scheduling, gradient accumulation, hyperparameter tuning, and instruction fine-tuning through practical demonstrations. Finally, you will evaluate fine-tuned models using metrics such as Accuracy, F1 Score, BLEU, ROUGE, and Perplexity, perform error analysis, and explore parameter-efficient fine-tuning techniques including LoRA and PEFT. By the End of This Course, You Will Be Able To: - Prepare high-quality datasets for fine-tuning AI models. - Apply supervised fine-tuning techniques to optimize model performance. - Analyze model quality using standard evaluation metrics and error analysis. - Evaluate fine-tuning strategies for different AI applications. Designed for AI engineers, machine learning practitioners, software developers, and data scientists, this course provides the practical skills needed to customize pretrained AI models for real-world applications.