Supervised Learning, Unsupervised Learning and Reinforcement Learning are three approaches of Machine Learning (ML). Supervised learning is supervised learning in which future developments can be inferred from the data
. Unsupervised learning is about drawing insights and inferences from the dataset and in reinforcement learning, the system
receives recognition when it has performed an appropriate action. Supervised learning is aboutmapping
therelationships between input and output variables
These are classified and learning algorithms are derived from them to support future decisions. Using training data and refined classification with group membership, the system improves the learning algorithm. During training, the system contains datasets
with group membership from which the system can select the group features. The models built by supervised learning from existing data. Supervised learning is used in autonomous driving, chatbots, recognition systems, expert systems and robots, among others.