Dissecting Data Mining, Statistics, Machine Learning and Artificial Intelligence

Data mining is a practice of applying algorithms (mostly Machine learning algorithms) with the data available from domain to solve domain related problems.

Statistics is a study of how to collect, organizes, analyze, and interpret numerical information from data. Statistics can slip into two taxonomy called Descriptive statistics and Inferential statistics. Descriptive statistics involves method of organizing, summering and picturing information from data. Inferential statistics invokes method of using information from sample to draw conclusion about the population.

Machine learning uses statistics (mostly inferential statistics) to develop self learning algorithms.

Data mining uses statistics (mostly Descriptive statistics) on results obtained from algorithms, it used to solve the problem.

Artificial Intelligence is a science to develop a system or software to mimic human to respond and behave in a circumference. As field with extremely broad scope, AI has defined its goal into multiple chunks. Later each chuck has become a separate field of study to solve its problem.

Natural language processing is another such field emerged from AI goal to help machine to communicate with real human.