Supervised learning vs unsupervised learning

Supervised Learning:

Supervised learning is a machine learning approach that uses labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.
The two types of supervised learning are Classification and Regression.
Classification: This type of supervised learning uses algorithms that are mainly used to predict the class or category.
Example- Logistic Regression.
Regression: This type of supervised learning uses algorithms that are mainly used to predict the numerical values.
Example- Linear Regression.

Unsupervised Learning:

Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms discover hidden patterns in data without the need for human intervention (hence, they are “unsupervised”).
The two types of unsupervised learning are Association and Clustering.
Association: This type of unsupervised learning method uses different rules to find relationships between variables in a given dataset.
Clustering: It is a data mining technique for grouping unlabeled data based on their similarities or differences.