Difference between Artificial Intelligence vs Machine Learning vs Data Science

In this article, we will be discussing the key difference between technological words Artificial Intelligence vs Machine Learning vs Data Science. Many of us don't know the real difference so there is a misconception in people about these terms.

first, take a look at some verbose and technical definitions about these popular terms.

Artificial Intelligence is the simulation of human-like intelligence executed by machines, especially computer systems. These processes include learning (taking some information and based on previous knowledge), reasoning (using rules to reach approximate or definite conclusions) and self-correction.


Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions(without traditional coding), relying on patterns and inference instead.


Data science is not a single field instead of its a multi-disciplinary field that uses scientific methods,
procedurals, algorithms, and systems to give some knowledge and insights from structured and unstructured data.

To get the difference between these correlated terms we will take some points so that we can understand this little bit more clearly.

Parameters	Artificial Intelligence	Machine Learning	Data Science
Interpretation	From the above definition,  the AI is the system or environment where human-like behavior developed. This behavior developed using various techniques and technologies by the professionals.	Machine Learning is    one of the most important parts to achieve AI in the system using different different algorithms.	Data Science is also another important part to implement AI. It will lead to valuable insight into data to take important business decisions.
Integral Parts	AI has many integral parts to perform the task efficiently, like ML & DS.	ML has some important integral parts in which model creation using Deep learning are the important one along with model deployment.	In DS data pre-processing, exploratory data analysis is an important one. Although the part of ML also comes under DS except for the deployment part.
Job Profile	In this many jobs comes, or in contrary it can be said like it’s a whole department rather than a job profile.	Machine Learning Engineer is a job profile offered in some large scale company.	Data scientists are the job profile someone will have, who has this field-related knowledge, although in some small company this job profiles are not having defined role rather they need to perform more or fewer job roles in one position.
Scope	AI has wide spectrum to cover in each and  every field it can have scope of implementation.	Scope for this is mainly dependent over the Neural networks, deep learning and algorithms developments.	In DS the scope is dependent over the data analysis, visualization tools, and of course deep learning, etc.
Implementation	AI is getting implemented in fields like Home appliances, finance, automobiles, and various others.	ML has been the most important part which plays a major role to automate and implement AI previously mentioned field.	Like ML, DS is also being implemented by data scientists to effectively implement the AI.
Future Vision	The future vision of the AI that, it will change and affect human life more than anything in the past.	In ML the various studies and complex algorithms would be possible due to techniques improved in implementation in the future.	In future the visualization technique and also the understandability would become easier and way more fast than today.