Demystifying Machine Learning to a non-techie friend

Many times I've been asked by friends to explain what Machine Learning Model is

The term, Machine Learning, often mystifies its nature of computer science, as its name might suggest that the machine is learning as human does, or even better.

Despite the hope that one day we could have machines that think and learn the way that humans do, machine learning nowadays does not go beyond a computer program that performs the predefined procedures. What distinguishes a machine learning algorithm from a non machine learning algorithm, such as a program that controls traffic lights, is its ability to adapt its behaviours to new input. And this adaptation, which seems to have no human intervention, occasionally leads to the impression that the machine is actually learning. However, underneath the machine learning model, this adaptation of behaviours, is as rigid as every bit of machine instructions that are programmed by humans.

So what is a machine learning model ?

A machine learning algorithm is the process that uncovers the underlying relationship within the data.

The outcome of a machine learning algorithm is called machine learning model, which can be considered as a function F, which outputs certain results, when given the input.

Rather than a predefined and fixed function, a machine learning model is derived from historical data. Therefore, when fed with different data, the output of machine learning algorithm changes, i.e. the machine learning model changes.

For example, in the scenario of image recognition, one might train a machine learning model to recognize the object in the photos. In one case, one might feed thousands of images with and without dogs to a machine learning algorithm, in order to obtain a model that is capable to tell whether there is a dog in a photo. As a result, the input of the generated model would be a digital photo, and the output is a boolean value indicating the existence of a dog on the photo.