A DataCamp course that turns LLM theory into working code. Across about three hours of interactive exercises you identify transformer architectures, use pre-trained models and datasets from Hugging Face, and fine-tune models. Hands-on and practical for people who can already code Python and want to work with LLMs directly.
Good for: Getting hands-on with LLMs in Python via Hugging Face.
Less suitable if: You cannot code Python or want a no-code conceptual course.
Skills you'll gain
Large language modelsPythonHugging FaceTransformersFine-tuningNLP
Is this course right for you?
A good fit if you…
You can code Python
You want hands-on LLM work
You want to use Hugging Face models
Consider something else if you…
You cannot yet code Python
You want a no-code overview
You want deep ML theory
Requirements: Python; some ML familiarity. Fine-tuning may need GPU compute.
Realistic time: Around 3 hours.
About this course
Introduction to LLMs in Python turns LLM theory into working code. Across about three hours of interactive exercises you identify different transformer architectures, use pre-trained models and datasets from Hugging Face, and fine-tune and evaluate models for tasks like translation and text generation.
What you'll learn
Identify transformer architectures
Use pre-trained models from Hugging Face
Build pipelines for language tasks
Fine-tune an LLM on your data
Evaluate model performance
Apply LLMs to translation and generation
This course includes
3h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
What it costs
DataCamp runs on a subscription — roughly $14/month billed annually (more month-to-month), with the first chapter of each course free to try. A certificate of completion is included with the subscription. Fine-tuning larger models can require paid GPU compute, separate from DataCamp.
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