What is difference between Supervised learning and unsupervised learning in machine learning?

Supervised learning and unsupervised learning are two major types of machine learning algorithms.

Supervised learning is a type of machine learning where the algorithm learns to predict output based on input features with the help of labeled data. In supervised learning, the algorithm is provided with a labeled dataset, which means that the correct output is provided for each input in the dataset. The algorithm learns from this labeled data to make accurate predictions on new, unseen data.

Examples of supervised learning include classification tasks, such as predicting whether an email is spam or not spam, or predicting whether a patient has a certain disease based on their symptoms. Regression tasks, such as predicting the price of a house based on its features, are also common examples of supervised learning.

Unsupervised learning, on the other hand, is a type of machine learning where the algorithm learns to find patterns and relationships in the data without being explicitly told what the correct output should be. Unsupervised learning algorithms are provided with an unlabeled dataset, and they learn to group or cluster similar data points together based on their similarities or patterns in the data.

Examples of unsupervised learning include clustering tasks, such as grouping similar customers together based on their purchasing habits, or finding topics in a large set of documents. Anomaly detection, such as identifying fraudulent credit card transactions, is also a common example of unsupervised learning.

Overall, the main difference between supervised and unsupervised learning is the availability of labeled data. Supervised learning relies on labeled data to make accurate predictions, while unsupervised learning does not require labeled data and can identify patterns and relationships in the data on its own.