📚 ML Algorithms: Linear Regression, Logistic Regression & K-Nearest Neighbour

📖 Syllabus Dot Point

Describe types of algorithms associated with ML
Including: linear regression, logistic regression, K-nearest neighbour

In machine learning, an algorithm is the mathematical method used to learn patterns from data and make predictions. Different algorithms are designed for different kinds of output. Some algorithms predict a number, some predict a category, and some predict by comparing a new data point to examples that are already known.

This lesson introduces three important algorithms that students must be able to describe clearly. That means you should know what each algorithm does, what type of output it produces, how it makes its prediction, and what kinds of tasks suit it best. Software engineers do not just memorise algorithm names — they learn the characteristics that make each one useful.

Linear regression predicts a continuous numerical value by fitting a straight line through data. Logistic regression predicts a category by calculating a probability and then applying a threshold. K-nearest neighbour (KNN) predicts by looking at the most similar nearby examples and taking a vote.

🎯 What you must be able to describe

For every algorithm in this lesson, focus on four things: what goes in, what comes out, how the prediction is made, and what kind of problem it suits. This structure helps you write accurate exam responses.