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I have recently started exploring the field of machine learning ML. I think I understand the difference between ML and AI at high level, but I wanted to understand more accurately the differences between these commonly used concepts.
After some research, I came out with the following boundaries for these domains. Am I correct here or is something inaccurate? AI :- AI is the intelligence given to machine to simulate the human intelligence like responding and behaving in a circumstance.
It's a superset of ML. Data Mining:- Data Mining is about using Statistics as well as other programming methods to find patterns hidden in the data so that you can explain some phenomenon. Data Mining builds intuition about what is really happening in some data and is still little more towards math than programming, but uses both.
Machine Learning uses Data Mining techniques and other learning algorithms to build models of what is happening behind some data so that it can predict future outcomes. An example for the latter is when neuronal nets are used in simple task based learning e. Data mining is somewhat broader than your definition, because it's not only about explaining phenomenon, but also discovering phenomenons.
Machine learning and deep learning can help for this purpose. But they also have applications that are not related to data mining e. DL can be used for NLP and machine translation, without having as goal to mine data and discover unknown grammar rules.
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