In this edition, we’re exploring the world of word embeddings and finally making sense of why they’ve become the backbone of modern NLP. You’ll get a clear feel for what embeddings actually represent, explore how Word2Vec learns meaning through prediction and why that tiny training task uncovers so much structure. And to bring it all together, you’ll learn how to think like an embedding model itself, giving you the intuition you need before stepping into the world of transformer-based NLP.