Classification: This technique involves sorting data into predefined categories or classes. For example, in a customer database, classification can be used to segment customers based on buying behavior or demographic characteristics.

Clustering: Unlike classification, clustering groups data into clusters based on similarities without predefined labels. This technique is useful for market segmentation, where customers are grouped based on similar purchasing habits.

Association Rule Learning: This method is used to discover relationships between variables in large datasets. A classic example is market basket analysis, where associations between different products purchased together are identified.

Regression: Regression techniques predict a numerical value based on the relationship between variables. For example, regression analysis can forecast sales based on historical data and external factors like seasonality.

Anomaly Detection: This technique identifies outliers or anomalies in a dataset that may indicate unusual behavior, such as fraudulent transactions or network intrusions.

Decision Trees: Decision trees are used to create a model that predicts the value of a target variable based on several input variables. They are particularly useful for decision-making processes in business.

Neural Networks: These are complex models inspired by the human brain that can detect intricate patterns in data. Neural networks are particularly effective in image and speech recognition.