Supervised learning
Supervised learning algorithms are trained using labeled data. Supervised learning model takes direct feedback to check if it predicting correct output or not.
Supervised learning model predicts the output.
In supervised learning, input data is provided to the model along with the output.
The goal of supervised learning is to train the model so that it can predict the output when it is given new data.
Supervised learning needs supervision to train the model.
Supervised learning can be categorized in classification and Regression problems.
Supervised learning can be used for those cases whare we know the input as well as corresponding outputs.
Supervised learning models products an accurate result.
Unsupervised learning
Unsupervised learning algorithms are trained using unlabeled data.
Unsupervised learning model does not take any feedback.
Unsupervised learning model finds the hidden pattern in data.
In Unsupervised learning, only input data is provided to the model.
The goal of Unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset.
Unsupervised learning does not needs any supervision to train the model.
Unsupervised learning can be classified is Clustering and Associations problems.
Unsupervised learning model mat give less accurate results as compared to supervised learning.
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 Clustering, KNN, and Apriori algorithm.