Types of Machine Learning (ML)

Types of Machine Learning (ML)

Machine Learning Algorithms helps computer system learn without being explicitly programmed. These algorithms are categorized into supervised or unsupervised. Let us now see a few algorithms −

Supervised machine learning algorithms

This is the most commonly used machine learning algorithm. It is called supervised because the process of algorithm learning from the training dataset can be thought of as a teacher supervising the learning process. In this kind of ML algorithm, the possible outcomes are already known and training data is also labeled with correct answers. It can be understood as follows −

Suppose we have input variables x and an output variable y and we applied an algorithm to learn the mapping function from the input to output such as −

Y = f(x)

Now, the main goal is to approximate the mapping function so well that when we have new input data (x), we can predict the output variable (Y) for that data.

Mainly supervised leaning problems can be divided into the following two kinds of problems −

• Classification − A problem is called classification problem when we have the categorized output such as “black”, “teaching”, “non-teaching”, etc.

• Regression − A problem is called regression problem when we have the real value output such as “distance”, “kilogram”, etc.

Decision tree, random forest, knn, logistic regression are the examples of supervised machine learning algorithms.