What is Machine Learning? What are its algorithms?

 

Machine learning is a subset of artificial intelligence that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can be used to automatically improve the performance of a system by adapting to the data it is being fed.
There are many different types of machine learning algorithms, but some of the most common are linear regression, support vector machines, decision trees, and neural networks. Each of these algorithms has its own strengths and weaknesses, so it is important to choose the right one for the task at hand.

Linear regression:
Linear regression is a simple machine learning algorithm that is used to predict the outcome of a linear relationship between two variables. It assumes that the data can be described by a linear equation, and it can be used to predict future values based on past data.

Support vector machines:
Support vector machines (SVMs) are a more complex type of machine learning algorithm that can be used to predict the outcome of a non-linear relationship between two variables. They are based on the idea of dividing the data into two groups, or "support vectors", that are as far apart as possible.

Decision Trees:
Decision trees are another type of machine learning algorithm that can be used to predict the outcome of a non-linear relationship between two variables. They are based on the idea of splitting the data into a series of decision nodes, where each node represents a possible decision that can be made.

Neutral Networks
Neural networks are a type of machine learning algorithm that are modelled after the workings of the human brain. They are used to predict the outcome of a complex non-linear relationship between many variables.