Machine learning is a subset of artificial intelligence (AI) that deals with the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, but instead rely on patterns and inference. It showcases a system's ability to learn and improve from experience.
Machine Learning in practice
Machine learning involves the collection and analysis of large amounts of data to identify patterns. Once these patterns are identified, models are developed to predict future outputs. Machine learning encompasses various techniques such as regression, classification, clustering, and reinforcement learning, among others.
The machine learning process typically begins with inputting training data into the selected algorithm. The model is then trained on this data, where the machine makes predictions or decisions without being specifically programmed to do so. Over time, the machine continually learns from this process, improving its performance and accuracy. This iterative aspect of machine learning is what makes it adaptable when exposed to new data.
Machine learning algorithms can be categorized into supervised learning, where the model is trained on labeled data, and unsupervised learning, where the model finds structures and patterns from unlabelled data. There is also semi-supervised learning where a small amount of labeled data is used alongside a large amount of unlabeled data.
Furthermore, another type of learning is reinforcement learning, where an agent learns to behave in an environment, by performing certain actions and observing the results/rewards. This approach allows the system to learn and make suitable decisions over time.
The applications of machine learning are extensive and are increasingly becoming part of everyday life, including in search engines, email filters, voice recognition systems, and recommendation systems.
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