Machine Learning Foundations
A jargon-free introduction to what machine learning is, its three main types, and the data-to-model workflow behind every ML project.
Training, testing, and the split
How do you know the model actually learned? Suppose you train a model and it answers every example you gave it correctly. Is it good? You cannot tell yet — because it might have simply memorised those examples rather than learning a pattern that works on new ones. To find out, you need to test […]
What an accuracy figure is hiding
You will be shown numbers. Here is how to read them You do not need to build models to be useful here. You need to be the person in the room who asks the three questions that separate a real result from a demo. 1. Measured on what? A score on the data the model […]
Your first honest ML conversation
Turning the whole course into one useful habit Whether you commission ML, work beside it, or are simply in the room, the same short frame turns a vague idea into a conversation that goes somewhere. The five questions, in order What decision changes? If no decision or action changes because of the prediction, there is […]