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What is the difference between Machine Learning vs Artificial Intelligence?

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Artificial Intelligence is a superset of Machine Learning. It aims to increase the success rate in achieving cognition rather than efficiency. It is used to simulate human intelligence in machines to solve complex issues. It focuses on decision making. Its main concern is to find the optimal solution to the problem. AI develops machines to mimic human actions.

AI can be divided into three types- Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI). ANI is used when you need to perform a single task with intelligence. AGI is efficient and can be used to perform multiple tasks while learning and improving on its own. ASI has super-intelligence which is better than normal human intelligence. It is used to perform tasks that are beyond human intelligence.

On the other hand, the main goal of Machine Learning is to increase learning accuracy instead of achieving successful results. It is used to learn from given data on particular tasks in order to maximize the machines’ performance. It allows machines to gain insights and learn new information from data. ML is concerned about understanding whether the obtained solution is optimal.

There are mainly two types of Machine Learning – supervised learning, and unsupervised learning. In supervised learning, the dataset is trained as per the target variable in order to predict it. On the other hand, in unsupervised learning, the training of data sets is based on previous learning rather than a target variable.

To learn more about the difference between Machine Learning and Artificial Intelligence, watch this video tutorial. Also, you can join Intellipaat's best machine learning course.

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