Before going with the relation of Data Science, Machine Learning and Deep Learning.
So what we understand from the above image that these (Data Science, Machine Learning and Deep Learning.) are subset or related to Artificial Intelligence . Let's go with first thing first .
Artificial Intelligence :
Before going into the technical term we are going to understand Artificial Intelligence in Layman’s Term -
Artificial Intelligence in a nutshell enables the machine/computers to think.
I had a notion that artificial intelligence (AI), was about robots taking over the world by being able to do the same things that we, as humans could do. While this is part of the truth, this is not entirely what artificial intelligence is all about. As we know, half truth is almost no truth.
Artificial intelligence (AI) is therefore, based on the idea of the capability of a machine or computer program to think(reason), understand and learn like humans. We can also say that artificial Intelligence is the study of the possibility of creating machines able to apply knowledge received from data in manipulating the environment.
Real life example of AI:
Self-driving car (autonomous car or driverless car):
A self-driving car (sometimes called an autonomous car or driverless car) is a vehicle that uses a combination of sensors, cameras, radar and artificial intelligence (AI) to travel between destinations without a human operator.
Netflix: Netflix starts making movie suggestions to you , based on your watch pattern. So, if you love romantic movies it would make suggestions of romantic movies ( not me i am a thriller lover hahaha …)to you, based on what I machine/computer know about you. This is human intelligence.
How is this possible? Artificial intelligence. This is a very general example of Artificial intelligence.
What is Machine learning? Again going with the layman’s terms Machine learning is ths subset of Artificial Intelligence provides us statistical tool to explore data . In Machine learning -: - The system is able to make prediction or decision based on past data . - Needs only small amount of data . - Works well with low end computers .
Types of Machine Learning:- 1.Supervised Learning(label data /past data) 2.Unsupervised Learning(clustering) 3.Reinforcement/Semi-Supervised Learning( no raw data is given as input instead reinforcement learning algorithm have to figures out the situation on their own.)
Real life example of ML:
“We all get spam mails. These are always filtered out by gmail for example. Also, mails are categorised as promotions and social, as well as other categories based on the mail service you use. How has gmail learnt to do this? Machine learning! Don’t forget ML is part of AI.”
What is Deep Learning? Again going with the layman’s terms Deep Learning is the subset of Machine learning where we can make machine to learn like human . The field of artificial intelligence is essentially when machines can do tasks that typically require human intelligence.
Deep learning is sometimes called deep neural networks(DNN) because it makes use of multi-layered artificial neural networks to implement deep learning.
Real life example of DL:
Image Recognition: Image recognition is one of the most common uses of machine learning. There are many situations where you can classify the object as a digital image.
Speech Recognition: Speech recognition is the translation of spoken words into the text. It is also known as computer speech recognition or automatic speech recognition.
What is data science?
Data Science is not fully Artificial intelligence, however portions of Data science intersect with Artificial intelligence.
What I have presented here are the insights of How are Data Science, Machine Learning and Deep Learning Related. I hope you learned something today.
Always remember that solid business questions, clean and well-distributed data always beat fancy models.
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