Course 1 · Module 1
Machine Learning and Language Models
This module covers the ideas underneath every AI chatbot: learning from examples, neural networks, and predicting the next piece of text. You'll be able to explain how a system can acquire an ability nobody programmed into it and how it writes a reply one token at a time. These ideas are worth having on their own, because most claims about what AI "knows" or "thinks" make sense only once you know what training and prediction are.
Module objectives
- Explain how a machine learning system learns patterns from examples instead of following written rules.
- Describe how training adjusts a neural network's parameters to reduce its errors.
- Explain how a language model turns text into tokens and generates a response by predicting what comes next.
Lessons
- Lesson 1 Machine Learning Basics: Learning from examples instead of rules You'll look at the difference between software that follows rules a person wrote and software that picks up patterns from examples. You'll be able to say which kind of system… 61111h 3m
- Lesson 2 Machine Learning Basics: Neural networks and how training adjusts them You'll see what a neural network is made of and how training changes it, a little at a time, until its predictions improve. You'll be able to explain what people mean when they… 61111h 3m
- Lesson 3 Machine Learning Basics: Tokens, meaning, and next-token prediction You'll follow text into a language model and back out: how it's broken into tokens, how meaning is represented as position, and how a reply is produced by predicting one token… 611111h 33m