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 Google for Developers
Google's Introduction to Large Language Models is a short, free course explaining LLMs, how they're trained, and how to use them. It's part of Google Cloud's AI learning path. The course covers foundational concepts without deep technical detail. After finishing, you understand LLM architecture and can use them effectively. The course is beginner-friendly and takes under an hour. The main limitation: it's a high-level overview. For implementation details or building with LLMs, take prompt engineering courses or LLM frameworks.
84% of developers are using or planning to use AI tools in their development process, up from 76% a year earlier, and the large language models this course explains are what those tools run on (source: Stack Overflow Developer Survey 2025, Sentiment and Usage section, survey.stackoverflow.co/2025/ai).
Google's overview of large language models (LLMs). Covers what LLMs are, their use cases, prompting techniques, and how to tune LLMs for specific tasks using Google tools.
Generative AIBeginner
freeCodeCamp's generative AI course covering prompt engineering, RAG, model deployment, and AI agents. Taught by Andrew Brown. Completely free on YouTube.
Generative AIIntermediate
Harvard's introduction to AI with Python. Covers search, knowledge representation, uncertainty, optimisation, machine learning, neural networks, and NLP.
Generative AIIntermediate
Learn to build and deploy with transformers and large language models in this 12-chapter course from Hugging Face. You'll start with NLP fundamentals, then move into modern LLM techniques: tokenization, fine-tuning, reasoning models, and building shareable demos. By the end, you'll know how to use pre-trained models, customize them on your own data, and integrate them into real applications. Best if you have some Python experience; the course assumes basic familiarity with machine learning. Fair warning: LLM chapters move fast, so budget time for rereading.