Tokens & Embeddings Explained: NLP Basics | AI Explained #8

Tokens & Embeddings Explained: NLP Basics – episode 8 of AI Concepts Explained, a free beginner-friendly AI and machine learning course by Irfan Hayat. How do computers read text? Irfan Hayat explains natural language processing basics: tokenization, subword tokens, vocabularies, word embeddings and cosine similarity, with animated diagrams and Python code. What you'll learn • Why text must become numbers • Tokenization: words, characters and subwords • Embeddings as points in meaning space • Measuring similarity with cosine similarity in Python • Where embeddings are used today Chapters 00:00 Intro 00:42 Why text must become numbers 01:17 Tokenization 02:02 Embeddings: meaning as vectors 02:40 Similarity in Python 03:20 Where embeddings are used 03:56 Byte pair encoding in Python 04:48 Counting tokens and cost 05:40 Embedding arithmetic 06:33 Mini project idea 07:20 Quick quiz 08:09 Recap 08:38 Next: Transformers All slides, code examples and explanations are original. Narration uses a synthetic (text-to-speech) voice reading the instructor's original script. No music, third-party footage or logos are used. ▶ Full series: AI Concepts Explained playlist #NLP #ArtificialIntelligence #MachineLearning