A ‘hello world’ equivalent program for Machine learning would be to use the neural network to model XOR. These days neural nets are often the first ML concept to be learned (not counting stuff like genetic algorithms and linear regression).
XOR is the operation that kicks out a 0 if two binary values are the same, but 1 if they are different. It is expressed like this:
a b XOR
0 0 0
0 1 1
1 0 1
1 1 0
Why is it important?
A single layer neural nets can only model linear functions/linearly separable data.2 layer neural nets can model data that is not linearly separable.
Let’s have a look at what it means in this context:
How can you make a linear function (which doesn’t bend around and cross the same space in a dimension, rendering multiple values at the same points on one dimension) which can separate activated data points from the zeroes?
Hence a 2 layer neural net is required.
You can find implementation and explanation of XOR here