Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed. It is quite vast and rapidly growing field. If we look around us, machines are involved everywhere in our day to day lives. Machine learning can be differentiated in two stats that is between supervised and unsupervised learning.
1. Supervised machine learning: The program is “trained” on a pre-defined set of “training examples”, which then facilitate its ability to reach an accurate conclusion when given new data.
2. Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
Machine learning builds mostly on statistics. So while training the machine to learn we have to apply the statistically significant random samples as training data. If the training set is not random we run the risk of the machine learning patterns that are not actually there.