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This course introduces large language models (LLMs) to data professionals, covering model architecture, tokenization, embeddings, fine-tuning, and use cases. Learners explore how LLMs fit into the modern data stack, enable natural language querying, and support data storytelling and summarization. Practical labs involve using APIs to analyze structured and unstructured data. The course also discusses evaluation metrics, performance considerations, and responsible AI usage. By the end, learners can integrate LLMs into data workflows for enhanced analytics and automation.
Russ Thomas
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