So, without further ado, let's get stuck in. Machine learning is a type of artificial intelligence that involves developing algorithms and models that can learn from data and then use what they've learned to make predictions or decisions. It aims to make it possible for computers to improve at a task over time without being told how to do so. In traditional programming, a programmer writes rules or instructions telling the computer how to solve a problem. In machine learning, on the other hand, the computer is fed data and learns to recognize patterns and relationships within that data to make predictions or decisions. This data-driven learning process is called "training" and is a machine learning model.

Neural networks are a type of machine learning algorithm that are inspired by the way the human brain works. They are used to recognize patterns and make decisions based on input data. Although there are several different types of neural networks, and each has its own unique characteristics and applications. Let's discuss the three most prominent ones, starting with, An artificial neural network (or ANN) is a type of machine learning algorithm that is inspired by the way the human brain works. It is made up of a network of artificial "neurons," which are inspired by the way biological neurons in the brain work.