There are two types of machine learning techniques—supervised learning and Unsupervised learning:
Supervised learning: Based on some historic prelabeled samples, machines learn how to predict the future test sample, based on the following categories:
Classification: This is used when we need to predict whether a test sample belongs to one of the classes. If there are only two classes, it's a binary classification problem; otherwise, it's a multiclass classification.
Regression: This is used when we need to predict a continuous variable, such as a house price and stock index.
Unsupervised learning: When we don't have any labeled data and we still need to predict the class label, this kind of learning is called unsupervised learning. When we need to group items based on similarity between items, this is called a clustering problem. While if we need to represent high-dimensional data in lower dimensions, this is more of a dimensionality reduction problem.
Semi-supervised learning...