Probably one of the most talked about machine learning models, neural networks are computational networks built to model animals' nervous systems. Before getting too deep into the structure, let's take a look at the big advantages of neural networks.
The key component of neural networks is that it is not only a complex structure, it is a complex and flexible structure. This means the following two things:
Neural networks are able to estimate any function shape (this is called being non-parametric)
Neural networks can adapt and literally change their own internal structure based on their environment
Neural networks are made up of interconnected nodes (perceptrons) that each take in input (quantitative value), and output other quantitative values. Signals travel through the network and eventually end up at a prediction node.
Visualization of neural network interconnected nodes
Another huge advantage of neural networks is that they can be used for supervised learning...