Supervised learning VS Unsupervised learning

The main differences between Supervised and Unsupervised learning are given below:

Supervised Learning Unsupervised Learning
Supervised learning algorithms are trained using labeled data. Unsupervised learning algorithms are trained using unlabeled data.
Supervised learning model takes direct feedback to check if it is predicting correct output or not. Unsupervised learning model does not take any feedback.
Supervised learning model predicts the output. Unsupervised learning model finds the hidden patterns in data.
In supervised learning, input data is provided to the model along with the output. In unsupervised learning, only input data is provided to the model.
The goal of supervised learning is to train the model so that it can predict the output when it is given new data. The goal of unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset.
Supervised learning needs supervision to train the model. Unsupervised learning does not need any supervision to train the model.
Supervised learning can be categorized in Classification and Regression problems. Unsupervised Learning can be classified in Clustering and Associations problems.
Supervised learning can be used for those cases where we know the input as well as corresponding outputs. Unsupervised learning can be used for those cases where we have only input data and no corresponding output data.
Supervised learning model produces an accurate result. Unsupervised learning model may give less accurate result as compared to supervised learning.
Supervised learning is not close to true Artificial intelligence as in this, we first train the model for each data, and then only it can predict the correct output. Unsupervised learning is more close to the true Artificial Intelligence as it learns similarly as a child learns daily routine things by his experiences.
It includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. It includes various algorithms such as Clustering, KNN, and Apriori algorithm.