This post explains Machine Learning, Data Mining, Supervised Learning and Unsupervised Learning concepts with simple words and fun drawings.
Data mining and machine learning used to be two cousins. They have different parents. Now they grow increasingly like each other, almost like twins. Many times people even call data mining by the name machine learning. The field of machine learning grew out of the effort of building artificial intelligence. Its major concern is making a machine learn and adapt to new information. The field of data mining grows out of knowledge discovery from databases. Data mining is focused on better understanding of characteristics and patterns among variables in large databases using a variety of statistical and analytical tools. It is used to identify relationships among variables in large data sets and understand hidden patterns that they may contain.
In supervised data mining techniques (supervised learning), there is a dependent variable the method is trying to predict. The classification and prediction/forecasting methods are supervised data mining techniques.
In unsupervised data mining techniques (unsupervised learning), there is no dependent variable. Instead, these techniques search for patterns and structure among all of the variables. A popular unsupervised method is association analysis (known in marketing as market basket analysis). Another popular unsupervised method is clustering (known in marketing as segmentation).