Fine-Tuning Techniques for AI Models
Completed by Laura Franciosa
September 14, 2026
5 hours (approximately)
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What you will learn
Prepare high-quality datasets for fine-tuning using data cleaning, tokenization, label engineering, and task formulation.
Apply supervised fine-tuning techniques to optimize pretrained AI models for domain-specific tasks and applications.
Analyze model performance using evaluation metrics, error analysis, and validation techniques to improve model quality.
Evaluate fine-tuning strategies, including LoRA and PEFT, to select efficient approaches for different AI use cases.
Skills you will gain
- Category: Artificial Intelligence
- Category: Large Language Modeling
- Category: Natural Language Processing
- Category: Data Cleansing
- Category: Prompt Engineering
- Category: Deep Learning
- Category: Supervised Learning
- Category: Model Training
- Category: Generative AI
- Category: Hugging Face
- Category: Data Quality
- Category: Python Programming
