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A Difference Between Machine Learning and Artificial Intelligence.

codeperfectplus profile image Deepak Raj Originally published at codeperfectplus.herokuapp.com Updated on ・3 min read

This article was originally published at:
http://codeperfectplus.herokuapp.com/difference-between-machine-learning-and-AI

Introduction

Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed. ML is one of the most exciting technologies that one would have ever come across. As it is evident from the name, it gives the computer that makes it more similar to humans: The ability to learn. Machine learning is actively being used today, perhaps in many more places than one would expect.

Deep learning is a Subfield of Machine learning and machine learning is a subfield of AI. As AI is a broad field we haven't achieved True AI Yet. There are a lot of things to discover in AI. Machine Learning Is basically Statics algorithms which feed on data and reach on a Pleatu after some iteration other hands deep learning aka neural network feeds on a large dataset. Neural Net(NN) use the concept of as our(Human) Brain works so It's much efficient for large dataset.

Artifical intelligence Robot

Artificial Intelligence is a technique that enables machines to mimic human behaviour. The ultimate aim of AI is to make intelligent machines that can perform human behaviour and take own smart decision.

Machine Learning is a sub-part of AI that uses statistical methods that enable machines to improve with experience.

Deep Learning is sub-part of ML that make use of Neural Networks (similar to neurons in human being) to simulate the human brain-like behaviour. But why do we need deep learning? The reason is ML algorithms can’t play longer in higher dimensions and/or higher number of observations data.

DL can be subdivided across different types of so-called artificial neural networks (also known as neural nets). The neural nets are critically layered and have such names as a CNN (convolutional neural network) typically used for vision (sight/pixel) processing or an RNN (a recurrent neural network) that has time-based functionality. DL techniques are employed within and across the fields of ML and the principal goal of ML is to support, develop, and encourage the growth of AI. If DL, ML, and AI were three matrushka dolls nestled inside one another then DL would be the smallest but the most powerful, ML would cover DL and be the most technically diverse and generically accessible and the big AI doll on top would be the all singing and dancing doll shaking bells and whistles that everyone loves to wonder about.

Another issue with ML is one has to fine-tune the number of parameters. In the case of deep learning, the neural network will decide on its own about important features. Basically deep learning mimics the way our brain tends to learn from previous experience.

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