Large language models (LLMs) like ChatGPT, Gemini, and Copilot have transformed how we interact with computers, and understanding how they work is key for anyone curious about modern artificial intelligence (AI).

In this course, we’ll introduce the core concepts that make LLMs so powerful, starting with deep learning and pretrained models. You’ll learn why Transformers have become the dominant architecture in natural language processing (NLP), and how they enable natural, human-like language generation.

Then we’ll dive into the three main components of a Transformer: embeddings, attention, and feedforward neural networks. You’ll see how encoder-only, decoder-only, and encoder-decoder models differ, and explore popular pretrained models like BERT, GPT, and T5.

By the end, you’ll have a solid conceptual grasp of how Transformers and large language models actually work, giving you the vocabulary and confidence to keep up with AI’s fast-evolving landscape.

If you want a visual, no-code introduction to the technology powering today’s most advanced language models, this is the course for you.

NOTE: This course is part of the Natural Language Processing in Python course, which is a more comprehensive overview of all the essential concepts for Natural Language Processing (NLP) in Python.

Who should take this course?

• Anyone curious about how LLMs like ChatGPT and Copilot work behind the scenes

• Beginners seeking an intuitive, visual explanation of Transformers and attention mechanisms

• Data professionals or AI enthusiasts wanting a solid foundation before exploring coding or model development

What are the course requirements?

• No coding experience required

• Some prior machine learning knowledge is helpful, but not necessary — we explain everything step-by-step with clear visuals