LLMs Foundations: Tokenization and Word Embeddings Models

Unlock the foundational secrets behind Large Language Models (LLMs) and AI chatbots in this hands-on, beginner-friendly course designed to demystify the core building blocks of modern NLP systems. Whether you're an aspiring developer, AI enthusiast, or seasoned professional seeking deeper insights, this course offers a clear, intuitive, and practical approach to understanding tokenization and word embeddings—two pillars of LLM architecture. You will gain a true understanding of how and why word embedding models and tokenization work the way they do.

Through over 6 hours of engaging video content, you’ll explore how tokenization transforms raw text into machine-readable units, and how word embeddings capture semantic meaning in multidimensional space. You’ll learn to build your own word embedding models using PyTorch, apply them to real-world tasks like question answering, and even develop a basic mini LLM from scratch. We break down complex mathematical concepts into digestible lessons, ensuring you grasp not just the “how,” but the “why” behind each technique.

By the end, you’ll have a solid foundation in the mechanics of LLMs and the confidence to apply these skills in practical AI projects. No advanced prerequisites—just basic Python and neural network knowledge. If you're ready to move beyond the hype and truly understand how AI chatbots work under the hood, this course is your launchpad.