What is the difference between supervised and unsupervised machine learning?

Generally, Supervised Machine learning or Supervised Machine at first, the machine has to be trained with well-labelled or well-aligned data. Most of the Supervised Machine Learning algorithms will be classification and regression algorithms. Some examples for Supervised Machine Learning,

• Linear regression
• Support Vector Machine
• Random Forest
• Naïve Bayes algorithm

In Unsupervised Machine Learning, as the name implies, it doesn’t need to be supervised. Unsupervised machine learning will feed itself by a set of untagged data.

• Hierarchical clustering
• K-means clustering
• K-nearest neighbours
• Anomaly detection
• Neural Networks

These machine learning algorithms will lend their hand in Data Science applications, Data Mining, Big Data Analytics, Artificial Intelligence, and Cloud Computing.

REFERENCE: https://doi.org/10.1007/978-3-030-22475-2_1