What is the Difference Between AI, ML, Statistics and Data Mining?

Know the difference between Artificial intelligence, machine learning, statistics, and data mining. It helps the organization in knowing how exactly the garnered dataset will be useful to them. Statistics is one of the most fundamental fields of study in mathematics that forms the base of the study for other computer science fields like Machine learning, Artificial Intelligence, etc.

In a world full of ignorance, we must not forget to do some reality check regularly. Having properknowledge of the topics that are a frequent trend gives us the power to determine how theworld is growing. Some of the regularly misinterpreted and misunderstood terms today areArtificial Intelligence, Machine Learning, Statistics, and Data Mining. if you are searching for the best machine learning course search in Delhi. you are the right place in the best Machine Learning Course in Delhi

Not denying the fact that these topics are not completely different from each other but thereexists a thin line that separates each of them. Each being closely related to the fields ofmathematics and computer science, these topics are the steps towards a smarter tomorrowwe’ve been waiting for.Data mining, machine learning, artificial intelligence, and statistics are all inter-related studiesthat are inspired by each other. The difference arises in their application as well as a way of usingeach of them. In order to understand the difference between them, we should first look intowhat each of them actually is.

Data Mining

As the name suggests, Data mining is involved with an in-depth analysis of huge datasets thatare available to find relations and patterns. The field of Data mining is most prevalent in businessanalytics sectors, stock markets, for improving sales, developing strategies, etc. It helps theorganization in knowing how exactly the garnered dataset will be useful to them. One of themajor advantages of data mining is that it understands which set of data is useful and relevant,and further work on that to make the required task a success. Retail, manufacturing, education,banking sectors are all using data mining today to boost their business models and producebetter outcomes.


Statistics is one of the most fundamental fields of study in mathematics that forms the base ofthe study for other computer science fields like Machine learning, Artificial Intelligence, etc. Thisfield of math is involved with an experimental set of data as well as real-world data, and it findsout ways to study both of them by using different measures like mean, variance, correlationcoefficient, skewness, distribution, testing, etc. Statistics is the heart of any business model. Nomodel can be created without making use of statistics as it helps to analyze and structurerequired as well as the available information.

Machine Learning

Machine Learning is one step higher in the department of computer science and works aroundteaching machines how to give outputs based on the previous input that was fed to it. Machinesdon’t learn but memorize with experience. They’re trained with an algorithm on a training set.

The model is then evaluated with evaluation metrics and checked for accuracy. It is then testedon a testing dataset or an unknown dataset to check if the model works properly.

This is how amachine learns and applies whatever it has learned on unknown datasets. A number ofalgorithms are used in machines based on the required problem statement. These algorithms are highly classified into 3 sections, Supervised Learning, Unsupervised Learning, and ReinforcementLearning.

Artificial Intelligence

The topmost layer after Deep Learning and Machine Learning is Artificial Intelligence. Artificialintelligence is the more complex version of Machine Learning involved with building suchtechnologies that have the capacity and capability of performing such computations that requirehuman intelligence.

Simply speaking, it builds machines that work like humans. This field isliterally changing the world. It has and is still making an impact in almost every sector of theworld. This field is currently being used mostly in facial recognition systems, speech recognitionsystems, security systems, gaming, agriculture, etc.

Difference between Machine Learning, Artificial Intelligence, Data Mining and Statistics

Since we now know what each of these fields means, we can delve deeper into knowing what thedifference between all of these is. Statistics is the field of study related to mathematics while therest of them belongs to Computer Science.

Even though statistics is not a computer sciencefield, it still forms the base of study for any statistical field here.Machine learning, data mining and artificial intelligence are all based on statistics.

The main aimof these fields is to find a relation between different datasets and models given to them which isthe fundamental of statistics. The statistical measures help us in understanding any model correctly.

The difference between the remaining three fields, Machine learning, artificial intelligence, anddata mining is closely related. They are arranged as follows:

Data Mining <= Machine Learning <= Artificial Intelligence

Data mining will always be the base as it is related to the preprocessing of datasets that will beused for building models in machine learning and Artificial intelligence. Hence, data miningrevolves around playing with big data and looking out for relations or patterns among them,doing research in related fields, etc.

Machine Learning and Artificial intelligence though seem tobe similar are actually very different techniques. While Machine learning means making themachine learn how to execute similar tasks based on previous experience, Artificial intelligencedeals with creating a simulation of human behavior.

Machines are not learners, they are memorizers. They are fed an input, an algorithm, and atesting set. They memorize what they are supposed to do in case of such datasets and theyperform. In the case of Artificial intelligence, machines are still memorizing and using machinelearning techniques but on a higher note, and are making advancement.

These machines are nowbehaving like humans. Artificial intelligence lies on top of the 3 layered diagrams consisting ofdeep learning, machine learning and AI itself. 

This is exactly how these topics are so closely related yet so different. Today’s world and theThe future is a gift of machines that are making our lives much easier and accessible.