ABSTRACT
A machine learning scientist aims is to gather the meaningful data by discovering the hidden patterns that rule this world and bind them in a procedure which helps in predicting the scenario in future new conditions. The machine learning models work with the input, on the basis of the relation among the features and predicts the output. The input has many forms, such as attributes and instances. How the input is configured is commonly known as the concept description. The conceptual data or input is used for exchanging in this book. It is the first thing a machine learns for the next stage of modeling. The instance is an individual, an independent, and a fixed value which is characteristic by the various features or attributes. Each attribute is a feature which can measure the instances or we can call them rows. The simplest way to represent output after machine learning is the same as the input.
