Machine Learning (ML) and Neural Networks (NN)… An Intuitive Walkthrough

The use of computer systems to learn and adapt without explicitly coded instructions; primarily utilized through statistical models and a machine's ability to draw inferences, and analyze certain patterns that may present themselves in data. The use of computer systems to mimic Biological Neural Networks (BNN); often utilized through a series of algorithms used to discover relationships between information, analyzed in an approach that mimics the inner workings of the human brain. A multi-layered Neural Network is referred to as a Deep Neural Network, lending itself over to Deep Learning (DL). I provided these definitions to multiple different people and got the exact same response each time… "I was able to understand absolutely nothing from that" To be honest, I can't really blame them. There's no doubt that these definitions present themselves in a way that's incredibly difficult to decode and understand. Simply reading these definitions adds very little value to our understanding of these incredibly complex fields. This article aims to take a deeper dive into these definitions to try and achieve a fundamental understanding of the inner workings of artificial intelligence, machine learning, and neural networks, as well as their relationships with one another intuitively. There are various different facets of machine learning, as well as how it functions. Hopefully, by the end of this article, your perception of ML will shift from one that associates it with magic, to one that's oriented around mathematics and logic.