Background

Let's first recap the premise of Machine learning and reinforce the purpose and context of learning methods. As we learned, Machine learning is about training machines by building models using observational data, against directly writing specific instructions that define the model for the data to address a particular classification or a prediction problem. The word model is nothing but a system in this context.

The program or system is built using data and hence, looks as though it's very different from a hand-written one. If the data changes, the program also adapts to it for the next level of training on the new data. So all it needs is the ability to process large-scale as opposed to getting a skilled programmer to write for all the conditions that could still prove to be heavily erroneous.

We have an example of a Machine learning system called spam detector. The primary purpose of this system is to identify which mail is spam and which is not. In this case, the spam...