Machine Learning

The types of machine learning

Machine Learning is a subfield of Artificial Intelligence which is a subfield of Computer Science.

Machine Learning is used to perform the tasks in the given time. The more programs it solved the more experienced it gained. It has improved with more data and more experience.

We can define Machine Learning as the systems that improve their performance in a given task with more and more experience or data.

There are three types of machine learning.

1. Supervised learning

Supervised learning is a type of machine learning, in which we already know the results and we will try to build the model that will predict the correct answers. In this type of machine learning technique, we have supervision or guidance to perform our task.

For example, we use supervised learning techniques for classification of problems. Such as, you build a model that can detect photos whether its a dog, or a cat.

2. Unsupervised learning

Unsupervised learning is a type of machine learning, in which we build models that will find structures in data or generate representation of the data. These models have been trained using unlabeled data.

For example data visualization or grouping similar items to form clusters is an example of unsupervised learning.

3. Reinforcement learning

Reinforcement learning is a type of machine learning algorithm, in which you have agent that learn in an environment by their mistakes and errors using feedback from its own actions.

The example of this type of machine learning technique is an AI agent like a self driving car that operates in an environment where feedbacks of good or bad choices are available.

You can also read Types of Artificial Intelligence and Computer Science and Its Subfields.