Machine Learning and its Types
Understand the foundational concepts of machine learning by exploring its main types: supervised, unsupervised, semi-supervised, and reinforcement learning. Learn how these approaches apply to real-world problems like prediction, clustering, and decision making, and discover key algorithms and applications that underline modern data science practices.
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Machine learning
Arthur Samuel was an American computer scientist and a pioneer in the field of gaming and artificial intelligence. Artificial intelligence is the intelligence exhibited by machines, unlike Natural intelligence. Machine learning is one of the sub-branches of artificial intelligence. Arthur Samuel coined the following definition of machine learning:
Field of study that gives computers the ability to learn without being explicitly programmed.
Machine learning, along with its sub-branch called deep learning, is used extensively in data science for predictive analytics. Once the data has been carefully wrangled and all the necessary insights have been extracted, we can have the following benefits of predictive modelling using machine learning.
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We can calculate the probabilities of occurrence of specific results in the future and take corresponding actions.
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We can automate the process of tagging numerous items in the relevant industry, which would have otherwise been only possible by involving a large amount of human labor.
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We can make real-time systems that trigger alarms before any significant accident occurs, predict stock prices, work on several forecasting problems, and many more examples in an automated way.
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We can optimize many industrial processes and save time and money.
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