Data Science

Difference Between Artificial Intelligence, Machine Learning and Deep Learning

If you are a professional in the field of Artificial Intelligence (AI), Machine Learning (ML) or Deep Learning, then you have probably heard these terms being used interchangeably to describe a technology that “learns” from data and has the ability to predict outcomes. But do they mean the same thing? Can one be used as an alternative for another? What does each term actually mean?

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Machine Learning

Machine Learning is a subset of AI. It is the science of getting computers to act without being explicitly programmed. Machine learning focuses on making data-driven predictions or decisions by building systems that can learn from data, identify patterns and make predictions about future behavior.

Machine Learning is used in many applications, including computer vision, speech recognition, natural language processing and predictive analytics.

Machine learning uses algorithms to present patterns in data, then trains itself on data sets so it can make predictions about future behavior.

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Artificial Intelligence

Artificial intelligence (AI) is a field of computer science dedicated to the creation of intelligent machines, especially computer systems capable of simulating human intelligence. AI research is also considered to be an area of study that deals with intelligent behavior in animals and machines, where it has been traditionally applied to many real-world tasks like decision making, problem solving, knowledge representation and learning.

AI research differs from robotics research in that robotics focuses on the design of robots and programming but AI focuses more on how the robot should function and learn from its environment as well as interact with humans or other robots.

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Deep Learning

Put simply, deep learning is a subset of machine learning which teaches machines to do what humans are naturally born with, which itself is a subset of artificial intelligence. It’s one of the most popular and powerful forms of AI today.

Deep neural networks are neural networks with many layers (or “hidden layers”), where each layer learns something about the data before passing it on to another layer as input. In deep learning, a model learns to perform tasks directly from sound, images, or text and can achieve best accuracy, sometimes more than human level performance.

Deep Learning is being widely used in industries to solve large number of problems like.

Image Recognition: A neural network identifies images of cats and dogs.

Self Driving Vehicles: Google’s self driving car is based on Machine Learning and Deep Learning algorithms. It can drive at a precision of 98% in dark, while its raining and in high terrain areas.

Producing Music: Deep Learning can be used to produce music by feeding in music patterns and letting it analyze on its own.

Voice Generation: Products like Alexa uses deep learning to generate voice and interact with humans.

Computer vision, natural language processing and pattern recognition.

It is important to understand the differences between these technologies.

In order for you to understand the difference between artificial intelligence, machine learning, and deep learning, it is important to know that AI is a broader term than machine learning or deep learning. Machine Learning (ML) and Deep Learning (DL) are subsets of Artificial Intelligence.

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Machine Learning: As its name suggests, ML refers to any software that learns from data without being explicitly programmed. In other words, ML algorithms can analyze patterns in large amounts of data and make predictions based on previous experience. For example, you may use an e-commerce site like Amazon and notice another product recommendation pop up after clicking on an item in your shopping cart these suggestions were made based on past purchases similar to yours.

Deep Learning: DL is a subset of ML that focuses specifically on neural networks mathematical models inspired by how neurons communicate in the brain which allow computers to learn tasks by analyzing large amounts of data similar to humans do through trial and error.

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Artificial intelligence is the entire field of study, machine learning is a subset and deep learning is a subset of that.

Artificial intelligence is a very broad term that encompasses the entire field of study. It’s what most people think of when they hear the name “artificial intelligence” and it’s also one of the most misunderstood.

Machine learning is a subset of artificial intelligence, which is a subset of deep learning. Machine learning is the study of data and algorithms that allow computers to learn (e.g., weather forecasting).

Artificial intelligence is a study of algorithms that allow computers to mimic human behavior (e.g., voice recognition). Deep learning falls under both machine learning and artificial intelligence since it deals with complex neural networks that model higher-level abstractions directly from sensory data through unsupervised learning in order to solve problems such as image recognition or speech transcription efficiently by training.

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