Unsupervised Learning

In unsupervised learning, the algorithm is not given any labeled data, and it has to find the patterns and relationships on its own. This kind of learning is useful when we don't have a clear idea about what we are looking for or when we have a large dataset that would take too much time and effort to label manually.

Clustering

One common unsupervised learning technique is clustering, where the algorithm groups similar data points together based on some similarity metric. For example, we can use clustering to group customers based on their purchasing behavior or group documents based on their content.

Dimensionality Reduction

Another unsupervised learning technique is dimensionality reduction, where the algorithm tries to find a lower-dimensional representation of the data while preserving its essential features. This is useful when we have a large number of features that make the dataset too complex to work with. For example, we can use dimensionality reduction to visualize high-dimensional data in 2D or 3D space or to speed up training in supervised learning tasks.

Popular Algorithms

Some popular algorithms for unsupervised learning include:

• k-means
• hierarchical clustering
• PCA (Principal Component Analysis)
• t-SNE (t-Distributed Stochastic Neighbor Embedding)
• autoencoders

Each of these algorithms has its own strengths and weaknesses and is suitable for different types of data and tasks.

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