What is supervised learning:
The use of labeled datasets distinguishes the machine learning strategy known as supervised learning. These datasets are intended to "supervise" or "train" algorithms to correctly classify data or forecast outcomes.
- Classification: Issues accurately classify test data into distinct groups, such as distinguishing apples from oranges, using an algorithm. Alternately, supervised learning algorithms can be applied in the real world to categorize spam in a distinct folder from your email.
- Regression: Utilises an algorithm to comprehend the link between dependent and independent variables. It is another kind of supervised learning technique. In order to estimate numerical values based on several data points, such as sales revenue projections for a certain business, regression models are useful.
What is unsupervised learning:
Machine learning algorithms are used in unsupervised learning to examine and group unlabeled data sets. Without the aid of humans, these algorithms find hidden patterns in data.
- Clustering: Clustering is a data mining method for putting unlabeled data into groups depending on how similar or dissimilar they are.
Learn more about supervised-unsupervised learning:
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