Machine Learning Models are chiefly classified into two categories – supervised learning and unsupervised learning. Both these learning models are further divided into different sub-categories. However, a preliminary understanding of the difference between the two is sufficient to further study them in detail.
Supervised learning is possible when the training data contains both the input and output values known as labeled data. This process is generally carried out under supervision. For instance, the data feed for an average “age-IQ” correlation. The inputs must contain values of both ages and respective IQs. The input (21,115), will instruct that the average IQ value of a 21-year-old is 115. This data is stored and analyzed. With an increasing number of inputs, the algorithm gradually tries to identify or formulate a pattern. The pattern is then used to predict the output for a given input. Supervised learning is further classified into two groups – regression and classification.
Unsupervised learning is where the machine works on its own by drawing patterns and inferences for data that only has input values. It handles unlabeled data. The two main methods of unsupervised learning are clustering and dimensionality reduction.