Computer systems may automatically enhance their performance over time thanks to a sort of artificial intelligence called machine learning. In order to generate predictions or judgements without explicit programming, a computer system, or model, must be trained on a collection of data. Machine learning may be classified as supervised learning, unsupervised learning, and reinforcement learning, among others.
The most prevalent kind of machine learning is supervised learning, in which a model is trained on a labelled dataset with known results or labels for each data point. The model can then generate predictions based on fresh, unforeseen data. Image classification, where a model is trained to recognise objects in photos, and regression, where a model is taught to forecast a continuous value, are examples of supervised learning.
When a model is trained on an unlabeled dataset, the aim of unsupervised learning is to discover patterns or structure in the data. Examples of unsupervised learning include clustering and dimensionality reduction. The act of putting related data points together is known as clustering, while the challenge of lowering the number of features in a dataset while preserving the crucial data is known as dimensionality reduction.
A model learns to make decisions through interaction with its environment and feedback in the form of rewards or penalties in a type of machine learning called reinforcement learning. It is utilised in robotics and game-playing applications.
In summary, Machine learning is a subset of Artificial Intelligence that allows computer systems to improve performance over time by learning from data. There are various types of Machine learning like supervised, unsupervised and reinforcement learning. Various algorithms are used to train the model and it is used in various fields like healthcare, finance, and e-commerce etc.
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