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Unsupervised learning is a type of machine learning in which an algorithm learns to identify patterns in unlabeled data without explicit guidance from a human teacher. Instead of being trained on a labeled dataset with pre-determined output values, unsupervised learning algorithms try to find structure and relationships in the data on their own.

The general process of unsupervised learning involves the following steps:

  1. Data preparation: Collecting and cleaning the data, and sometimes transforming it to make it suitable for unsupervised learning.
  2. Model selection: Choosing an appropriate algorithm or model for the problem at hand.
  3. Training the model: Feeding the unlabeled data into the algorithm or model to learn the underlying structure and patterns.
  4. Evaluating the model: Assessing the quality of the model’s output using metrics such as clustering accuracy, silhouette score, or reconstruction error.

Unsupervised learning algorithms can be broadly classified into two types: clustering and dimensionality reduction. Clustering algorithms group similar data points together based on their proximity in the feature space, while dimensionality reduction algorithms extract the most important features of the data by projecting it onto a lower-dimensional space.

Some popular unsupervised learning algorithms include k-means clustering, hierarchical clustering, principal component analysis (PCA), and autoencoders. These algorithms can be used for a wide range of applications, such as image compression, anomaly detection, and market segmentation.

Overall, unsupervised learning is a powerful and versatile technique in machine learning that can reveal hidden structures and insights in large and complex datasets.