In this article, we will explain the difference between three key concepts artificial intelligence, machine learning and deep learning to understand how they relate to the field of data science. First up,
artificial intelligence or A.I.? What is it?
Artificial intelligence is simply any code, technique or algorithm that enables machines to mimic, develop and demonstrate human cognition or behavior, we are in what many refer to as the era of weak A.I.
The technology is still in its infancy and is expected to make machines capable of doing anything and everything humans do. In the era of strong A.I. to transition from weak A.I. to strong A.I., machines need to learn the ways of humans.
Related:What is Artificial Intelligence?
The techniques and processes which help machines in this endeavor are broadly categorized under machine learning.
Machines learn and predominantly two ways. Their learning is either supervised or unsupervised
In supervised learning. Machines learn to predict outcomes with help from data Scientists.
In unsupervised learning machines learn to predict outcomes on the go by recognizing patterns in input data.
When machines can draw meaningful inferences from large volumes of data sets, they demonstrate the ability to learn deeply.
Deep learning requires artificial neural networks and ends, which are like the biological neural networks in humans. These networks contain nodes in different layers that are connected and communicate with each other to make sense of voluminous input data.
Deep learning is a subset of machine learning, which in turn is a subset of artificial intelligence.
The three technologies help scientists and analysts interpret tons of data and are hence crucial for the field of data science. To learn more about these technologies. comment below your queries.
Happy Learning!