A way of making machine learn

Category is termed as supervised learning because the process of an algorithm learning from the training dataset can be thought of as a teacher teaching his students. The algorithm continuously predicts the result on the basis of training data and is continuously corrected by the teacher. The learning continues until the algorithm achieves an acceptable level of performance. The labelled data set is the teacher that will train you to understand patterns in the data. The labelled data (raw dataset) set is nothing but the training data set.

Supervised learning is the machine learning task of inferring a function from labelled training data.

Supervised learning is the one where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output. Y = f(X)

The goal is to approximate the mapping function so well that whenever you get some new input data (x), the machine can easily predict the output variables (Y) for that data.

A Supervised Learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.

With the help of historical data, random sampling is carried out. Random sampling picks 70% and 30% of records. With 70%, the machine learning gets trained with the data. It is important to make sure the data is generalized and is not a specified one. Once the system is trained, it will provide a model (statistical model) which means that certain understanding has been at

Some Applications

Weather Apps: Predicts the upcoming weather by analyzing the parameters for a given time on some prior knowledge (when it's sunny, the temperature is higher; when it's cloudy, humidity is higher, etc.).

Biometric Attendance: In Biometric Attendance you can train the machine with inputs of your biometric identity – it can be your thumb, iris or ear-lobe, etc. Once the machine is trained it can validate your future input and can easily identify you.

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