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An introduction to prompt engineering for using the leading Large Language Models (LLMs) such as GPT, Claude, Gemini, etc. Also covers the underlying workings of these models, explaining concepts such as tokenization, embeddings, and fine-tuning.

Typically the first course taken. Does not require any prerequisite programming or math knowledge, though algebra would be helpful.

An introduction to the most foundational regression algorithms such as linear and logistic. Also covers neural networks, a smaller scale architecture that serves as the baseline for more well known large language models.

Typically an extension course for students who were interested by the first one. Requires some prerequisite programming (Python) and math (algebra).