Explore language-model curricula for tokenizers, model tooling, datasets, and evaluation, with guidance on Python and machine-learning prerequisites.
2 learning resources in this collection
Large language models involve more than writing prompts. Technical study introduces tokenization, model representations, data preparation, training or adaptation, and evaluation. These ideas are easier to approach with comfortable Python and introductory machine-learning knowledge.
The Hugging Face course offers a structured technical route. Check its prerequisites and the compute required for an exercise. Pay attention to dataset quality, evaluation, and model limitations rather than using a fluent output as the sole sign of success.
Run a small reproducible example and inspect both useful and failed outputs. Record the model, data, and settings so another learner can understand the result.
Udemy · Ed Donner and Ligency
Explore Ed Donner’s LLM engineering course on model selection, RAG, fine-tuning, and agents, with Python prerequisites and API cost notes.
Hugging Face · Hugging Face Team
A free technical course on transformers, tokenizers, datasets, model fine-tuning, and the practical Hugging Face ecosystem.