Overview of machine learning

Fundamentally, machine learning is the algorithmic process used to identify patterns and extract trends from data. By training specific machine learning algorithms on data, a machine learning model may learn insights that aren't immediately obvious to the human eye. A medical imaging model may learn to detect cancer from images of the human body, while a sentiment analysis model may learn that a book review containing the words good, excellent, and entertaining is more likely to be a positive review than one containing the words bad, terrible, and boring.

Broadly speaking, machine learning algorithms fall into two main categories: supervised learning and unsupervised learning.

Supervised learning

Supervised learning covers any task where we wish to use an input to predict an output. Let's say we wish to train a model to predict house prices. We know that larger houses tend to sell for more money, but we don't know the exact...