Below are the types of machine learning. They are the same in terms of results but different in how those are obtained.

Supervised Learning

This entails training a model on a labeled dataset and providing the proper output for each sample in the training set. The model is then used to forecast additional, previously unknown cases. As in training a machine to play chess by feeding labeled data of various chess moves and rules.

Unsupervised Learning

A common machine learning task is to provide a suggestion. For those who shop on Amazon, all recommended goods are based on the user's past purchases and viewed items. Unsupervised learning is being used by IT businesses to improve the customer experience by customizing suggestions.

Unsupervised learning entails training a model on an unlabeled dataset and letting the machine find patterns and correlations in the data on its own. Unsupervised learning might be applied in the context of chess to assess a huge dataset of prior chess games without being given any knowledge about the moves performed or the results of the games.

Semi-supervised Learning

Semi-supervised learning is classified as supervised or unsupervised learning since it uses both labeled and unlabeled data to train a model. It is especially beneficial when labeling a big dataset is expensive or time-consuming, as it lets the model learn from both labeled and unlabeled data.