Supervised Learning
Supervised learning is a type of machine learning algorithm that uses a known set of data (called the training set) to make predictions. It uses the data in the training set to build a model of the data, which it then uses to make predictions about new data. Supervised learning algorithms can be used for a variety of tasks, including classification, regression, and clustering.
Unsupervised Learning
Unsupervised learning is a type of machine learning algorithm that does not require a known set of data to make predictions. It uses an algorithm to explore a dataset for patterns and relationships that it can use to make predictions about the data. Unsupervised learning algorithms are often used for clustering, anomaly detection, and dimensionality reduction.
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- Supervised vs. Unsupervised Learning: What's the Difference? | IBM
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Supervised and unsupervised learning models work in unique ways to help businesses better engage … Supervised learning can be grouped into two main types:. - Artificial intelligence in healthcare: past, present and future – PMC
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field/type of AI. Machine learning is also often referred to as predictive analytics, or predictive modelling. Coined by American computer scientist Arthur … - Artificial intelligence (AI) algorithms: a complete overview | Tableau
There are three major categories of AI algorithms: supervised learning, unsupervised learning, and reinforcement learning. The key differences between these …
