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How Unsupervised Learning Algorithm Machine Learning Broke The Internet 💥 By Solution On Door 💥
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In machine learning, unsupervised learning algorithms are used to discover patterns and relationships in data without any prior knowledge of the output variable or the labels of the data. In other words, the algorithm is not provided with any predefined target variable to learn from.
Unsupervised learning is used when the data is not labeled, meaning the data scientist doesn't know the answer beforehand. In this case, the algorithm will automatically group and cluster the data based on similarities, allowing the data scientist to discover any underlying structure in the data.
Some popular unsupervised learning algorithms include:
1. Clustering algorithms: These algorithms group similar data points together based on some similarity measure. K-means clustering and hierarchical clustering are some popular examples.
2. Association rule learning: These algorithms find patterns and relationships between variables in a dataset. Apriori and FP-growth are some popular algorithms in this category.
3. Dimensionality reduction: These algorithms are used to reduce the number of features in a dataset while retaining as much information as possible. Principal Component Analysis (PCA) and t-SNE are some popular examples.
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