This is my understanding of "Machine Learning" in general

There are two sets of data:

1. In first data set, all the properties are known
2. In the second set, some properties are not known.

The goal of the machine learning is to find the value of the unknown properties of the second data set.

We do this by finding (or training) a suitable machine learning model (mathematical, logical or any combination of), that in the first data set, computes the value of the properties, which are unknown in second data set, with minimum error since we already know the real value of those properties.

Now, use this model to predict the unknown properties from the second data set.

@irene Yes, I missed that. The other goal of machine learning is to classify or group related data points together to identify the clusters by using the data properties.

model.fit(x_train)
model.predict(x_test)

Thats the machine learning.

I would define machine learning as finding knowledge of patterns and structure in data or observations.
This knowledge in turn enables predictions about previously unknown data or to infer structure were previously was none.

Machine learning algorithms are generally put into the categories supervised, unsupervised or semi-supervised learning.

In supervised learning the machine is given a defined input and output. It then tries to capture the correlation between the two. With the learned correlation it then tries to infer the output for input that was previously unknown to it.

In unsupervised learning the machine is just given input in which it tries to identify, structures and patterns. This can lead to a groups of similar data points, but also to different representations of the data itself.

Semi-supervised learning combines the two. For a part of the input the output is given, and for the rest not.