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With the rise of the digital world, a plethora of new terms and phrases have become commonplace, making it easy to become confused or lose track.
Businesses are wrestling with an entirely distinct language of tech lingo as a result of the rise of Big Data and analytics over the last several years.
It is likely to cause confusion, given the vast majority of individuals are unsure of the differences between such disparate concepts and techniques.
It's worth noting that Gregory Piatetsky-Shapiro coined the name KDD(Knowledge Discovery Process) for the first workshop on the same issue in 1989.
The main goal of this data collection is to find intriguing patterns and relationships among the many data points.
"Machine learning allows computer systems to learn without having to explicitly program," he added.


Machine learning algorithms are applied to increase efficiency and insightfulness of this data (but we'll expand on ML a bit later.)
The “Big Data” concept emerged as a culmination of the data science developments of the past 60 years.
Four V's of Big Data
Volume - the amount of data;
Velocity - the speed of processing data;
Variety - kinds of data you can collect and process;

AI consists of algorithms responsible for making a decision or a prediction about the next required tasks.
The data is analyzed by those algorithms, and as a result, it is represented by the proper and quick decisions.
DEEP LEARNING AND MACHINE LEARNING: EXPLAINING THE TERMINOLOGY
What do we mean under the term AI today
It is known that Artificial Intelligence is the set of algorithms that include just code and math and their task is to make the decision concerning the data.
So AI is the general term that can be used for any program that can perform smartly.