Deep Learning Vs Machine Learning | AI Vs Machine Learning Vs Deep Learning
Hello and welcome to Acadgild’s tutorial on data science.
In this video, we explain the difference between three key concepts artificial intelligence vs machine learning vs deep learning – to understand how they relate to the field of data science.
First up, artificial intelligence or AI! 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 AI”. 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 AI”.
To transition from weak AI to strong AI, machines need to learn the ways of humans. The techniques and processes, which help machines in this endeavor are broadly categorized under machine learning.
Machines learn in 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 (ANNs), 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.
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Thank you for watching and happy learning!
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