Supervised Learning definition

As mentioned in the Introduction, Supervised learning is one of the algorithms of Machine Learning. Before going to discuss further, let's first understand machine learning more. In machine learning, we try to predict the result of the data.
   In simple words, We first give the data whose result/solution is known to us.
   Then we build a model which is a relation/function that gives the relation between input data and it's solution. Then we provide the unknown data to the model and obtain the result. But, Do you think that we will get 100% accurate results? Just think about it.
   The answer is "NO". We always obtain the results with some errors. But If I say, I am happy with some error in my result then that error is allowed otherwise model is trained again and again until we do not get the required results. For obtaining more and more accurate results, we need a large amount of the data sets for training the model.

The figure shows it very nicely.

Supervised Learning: Suppose we want to classify whether the received email is spam or not. For finding that, the Machine will find some words like a credit card, amazing, free, etc to classify them as spam or not. This is problem of classification. Likewise, there are some other learning algorithms of supervised learning like Regression, Support Vector Machine (SVM), Neural Networks, Decision Trees and Random Forest.