They're not the same thing, but sometimes the perception they may lead to some confusion.So I thought to explain the difference, it would be worth writing a content.Both terms are very mutual when the subject is BigData analytics and the broader waves of technological change that are sweeping our world. The short and best definition of AI and ML is
1. AI: it is one of the border concepts ofa machine and it can able to carry the tasks
2. ML: it is one of the applications that are used in AI which can be used to access the data.
Difference between AI and ML
Older days
Our idea of what establishes AI has changed as technology and, most importantly, our understanding of how our mind works have progressed.Instead of increasingly complex calculations, AI work focused on imitating human decision-making processes and performing tasks in an increasingly rational way.Generalized AI:
In theory, systems or devices that can handle any task are less common, but this is where some of the most exciting improvements are taking place today.It's also the field that has led to Machine Learning's development. Often mentioned as a subset of AI, thinking of it as the current state-of-the-art is more accurate.Growth of Machine Learning
Two significant advances led to Machine Learning emerging as the vehicle driving AI growth forward with the speed it currently has.• Realization
• Growth of the internet
Realization: Instead of teaching computers, everything they need to know about the world and how to perform tasks, they could be prepared to learn for themselves.
Once these developments are in place, engineers noticed that it would be far more effective to code them to think like human beings rather than teaching computers and machines how to do everything, and then plug them into the web to give them access to all of the world's data.
Neural Network
Neural network development has been a key to teaching the computer how to think and understand the world as we do while retaining the inherent benefits that they hold over us like speed, lack of bias, and accuracy.A Neural Network is a computer system planned to work in the same way as a human brain classifies information.For example, recognizing images can be taught and categorizing them according to the elements they contain.The neural network works on a probability system – it can make statements, decisions or predictions with a degree of certainty based on data fed to it.Adding a feedback loop enables you to "learn" – by sensing or telling you whether your decisions are right or wrong; it changes the approach you take in the future.
Application of Machine Learning
Applications for Machine Learning can read the text and work out whether a complaint is made by the person who wrote it.They can also respond to a piece of music, determine whether to make someone happy or sad and find other parts to match the mood.The applications are mostly used in a neural network-based machine learning process. Moreover, machine learning algorithms are used to classify the system model and prediction level.
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