Supervised learning and unsupervised learning

In this article the difference between supervised learning and unsupervised learning which is used for machine learning in artificial intelligence and how can help in machine learning on training data set’s.

At first definition of supervised and unsupervised learning is defined

Supervised learning:-

                                In supervised learning the training data set’s is known means that the data source which classification/regression solution is already defined means that the classification and regression of data is correctly defined is called training data set’s. In supervised learning the such training data set’s is known. In supervised learning training data set’s is used for machine learning in artificial intelligence.
Example of supervised learning methods is just like Perceptron, LDA, SVMs, linear/ridge/kernel ridge regression.
In supervised learning the two major step’s is used which is “Training step”, “Prediction step”. In training step understand about classifier and regress-or from training data set’s, prediction step assign class labels and functional value to test data.

Unsupervised learning:-

                               In unsupervised learning training data set’s is not used for learning means that the data source which classification/ regression solutions is not predefined, In unsupervised learning the data clustering and dimension reduction is includes. We simply say that training without teacher is called unsupervised learning, here teacher means the training data set’s.