Author: Daniel Bourke
Machine learning is broad. The media makes it sound like magic. Reading this article will change that. It will give you an overview of the most common types of problems machine learning can be used for. And at the same time give you a framework to approach your future machine learning proof of concept projects.
First, we’ll clear up some definitions.
How is machine learning, artificial intelligence and data science different?
These three topics can be hard to understand because there are no formal definitions. Even after being a machine learning engineer for over a year, I don’t have a good answer to this question. I’d be suspicious of anyone who claims they do.
To avoid confusion, we’ll keep it simple. For this article, you can consider machine learning the process of finding patterns in data to understand something more or to predict some kind of future event.
The following steps have a bias towards building something and seeing how it works. Learning by doing.