Generative AIBeginner
Learn Generative AI in 23 Hours
freeCodeCamp's generative AI course covering prompt engineering, RAG, model deployment, and AI agents. Taught by Andrew Brown. Completely free on YouTube.
Free course on DeepLearning.AI
Finetuning Large Language Models
2 hours 4.7(41,000 reviews)Free
DeepLearning.AI's Finetuning Large Language Models teaches you to fine-tune pre-trained models for specific tasks using your own data. You learn when to fine-tune vs. prompt engineer, how to prepare data, and how to evaluate results. The course is free on deeplearning.ai. After finishing, you understand fine-tuning trade-offs and can adapt smaller, cheaper models to outperform larger ones on specific tasks. The main limitation: fine-tuning requires compute resources. This course teaches concepts with small examples; real fine-tuning needs a GPU and careful data curation.
About This Course
Learn when and how to fine-tune LLMs for your specific use case. Covers data preparation, training with the OpenAI API, evaluating fine-tuned models, and comparing against few-shot prompting. Free.
Course Details
- Platform
- DeepLearning.AI
- Language
- Generative AI
- Level
- advanced
- Duration
- 2h
- Certificate
- No
- Cost
- Free
Frequently Asked Questions
Is this course free?
Yes, free on deeplearning.ai.
When should I fine-tune vs. prompt engineer?
Prompt engineer first. Fine-tune when you need consistently better quality on a narrow task with lots of training data.
How much data do I need?
As few as 100-1000 high-quality examples can significantly improve task-specific performance.
Does fine-tuning change how the model thinks?
Yes. Fine-tuning changes model weights. It's different from RAG (which adds context) or prompting (which guides behavior).
What models can I fine-tune?
Open models like Llama, Mistral, and GPT-3.5 via OpenAI API. Not all models support fine-tuning.
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