With the evolution of technology and artificial intelligence, it may seem daunting to grasp these complex topics. We know that computers have learning capabilities that help them become stronger and more agile, but what does that mean? How do computers learn?
Let’s dive into how machine learning “learns” and what that looks like to humans. You can see a video version of this topic here.
How do you get a computer to learn? It sounds like a very difficult problem. But it's actually not that hard.
The whole idea is to give a computer some examples of something and let it build a model. There are two different ways that this might work. Machine learning learns through either a classification model or a regression model.
The classification model is represented with two different colors. You give the classification model a bunch of examples (i.e. green and blue dots) and you let the machine build the model. The model is the line between the dots. Once you have that model, it gives you the power to make predictions about new data.
The other core way that machine learning works is a regression model. In this case, your input is the x axis and your output is some prediction of a number.
For example, your x axis could be the square feet of a house and your y axis is the prediction of the value of that house, so then you can add new houses to the model and make guesses about them.
Take the example of teaching a machine to learn how to accurately detect faces.
You give the machine a whole bunch of examples of faces, a whole bunch of examples of non-faces, and it builds out models to distinguish them.
Phrasee uses machine learning to predict which marketing language leads to more opens, clicks and conversions.
What we're really modeling here is human behavior. From a consumer perspective, every time you get an email in your inbox, you make a decision on whether or not to click on that email. This is something that can be modeled surprisingly accurately with machine learning technology.
Learn more about how it works here.