Foundations of Large Language Models: From N-grams to LLMs

About this course

This course contains the use of artificial intelligence.

Large language models like ChatGPT and Claude can feel like magic. This course takes the magic away, step by step, so you understand what is really happening when a model writes a sentence.

We start with the simplest possible idea: predicting the next word by counting. From there we build up, one concept at a time, until you can explain how modern LLMs are built, trained, and turned into helpful assistants.

What you will learn:

• How text is broken into words and tokens
• How n-gram language models assign probabilities, and why they fall short
• How embeddings turn words into numbers that capture meaning
• How neural networks learn from data
• How attention and transformers let models use long context
• How pretraining and post-training create a helpful assistant
• Where to go next: retrieval, agents, and interpretability

How the course works:

Each lesson is short and focused, with clear visuals, worked examples, and plain-language explanations. We begin with the basics and save the more advanced topics for later sections, so you always know why a new idea is needed before you meet it.

Who this course is for:

College students, developers, analysts, and managers who use AI tools and want a solid mental model of how they work. No machine learning background is required, only curiosity and basic high-school math.

Note: the narration in this course is AI-generated.

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