Machine learning is an extension of the concept of linear regression in 2-D space to 9999999-D space. They also make the line more then just a "line" the equation produces more of a best fit complex curve in multi-dimensional space. This is the actuality of what programmers in this area do.
Much of that space consists of pairs of data that aren't relevant. So to understand it in terms of the analogy of the line in 2D space... The only thing that's relevant are points near the line.
For example if we use the line to represent housing costs(Y) over time(X). You can take a bunch of housing prices sold over time and plot it on the graph as dots. Linear regression will form a line that best fits those dots. Not EVERY single section on the plane matters though. Only the dots matter and the space that's very near the trend line have anything meaningful in terms of data.
Much of that space consists of pairs of data that aren't relevant. So to understand it in terms of the analogy of the line in 2D space... The only thing that's relevant are points near the line.
For example if we use the line to represent housing costs(Y) over time(X). You can take a bunch of housing prices sold over time and plot it on the graph as dots. Linear regression will form a line that best fits those dots. Not EVERY single section on the plane matters though. Only the dots matter and the space that's very near the trend line have anything meaningful in terms of data.