Machine Learning is a part of Computer Science where the efficiency of a system improves itself by repeatedly performing tasks by using data instead of explicitly programmed by programmers. Further let us understand the difference between the three techniques of Machine Learning- Supervised, Unsupervised and Reinforcement Learning.

Supervised Learning

Consider yourself as a student sitting in a classroom wherein your teacher is supervising you, “how you can solve the problem” or “whether you are doing correctly or not”. Likewise, in Supervised Learning input is provided as a labelled dataset, a model can learn from it to provide the result of the problem easily.

Types of Problems

Supervised Learning deals with two types of problem- classification problems and regression problems.

Classification problems

This algorithm helps to predict a discrete value. It can be thought, the input data as a member of a particular class or group. For instance, taking up the photos of the fruit dataset, each photo has been labeled as a mango, an apple, etc. Here, the algorithm has to classify the new images into any of these categories. Examples:

• Naive Bayes Classifier
• Support Vector Machines
• Logistic Regression

Regression problems

These problems are used for continuous data. For example, predicting the price of a piece of land in a city, given the area, location, number of rooms, etc. And then the input is sent to the machine for calculating the price of the land according to previous examples. Examples:

• Linear Regression
• Nonlinear Regression
• Bayesian Linear Regression