There are three types of Machine Learning, which include:

• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning

Supervised Learning

Supervised Learning is the most common type of Machine Learning. It involves training the algorithm with a “labelled” dataset. The labelled training data helps the Machine Learning algorithm make accurate predictions in the future.  

A practical example of supervised learning is training a Machine Learning algorithm with pictures of an apple. After that training, the algorithm is able to identify and retain this information and is able to give accurate predictions of an apple in the future. That is, it will typically be able to correctly identify if an image is of an apple.

Unsupervised Learning

In Unsupervised Learning, the training data is NOT labelled or named. The unlabeled data are used in training the Machine Learning algorithms and at the end of the training, the algorithm groups or categorizes the unlabeled data according to similarities, patterns, and differences.

This type of Machine Learning can help in grouping and organizing data in such a way that you can come in and make sense of the grouped data.

A practical example is training a Machine Learning algorithm with different pictures of various fruits. The algorithm finds similarities and patterns among these pictures and is able to group the fruits based on those similarities and patterns.

Reinforcement Learning

In this case, the algorithm discovers data through a process of trial and error. Favorable outputs are reinforced and non favorable outcomes are discarded. Over time the algorithm learns to make minimal mistakes compared to when it started out.