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Unsupervised Learning

Understand unsupervised learning principles, focusing on clustering with K-means and dimensionality reduction techniques like PCA. Learn to identify patterns in unlabeled data and implement these methods using Python's sklearn library for practical machine learning applications.

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Unsupervised learning aims to find patterns and structures within the given data. Learning algorithms in this category work on the input features without labels, i.e., the features are provided to unsupervised methods with no corresponding output labels.

The figure above differentiates supervised and unsupervised learning. Supervised learning (left) shows two-dimensional data points (xx and yy features) that belong to either the green circle class or blue square class. During the training/learning, we find the best separator (pink line) between these two classes. However, in unsupervised learning (right), two-dimensional data points don’t have any associated label. Here, we aim to find data patterns or clusters based on the resemblance between data points. Two different clusters, shown as rotated ellipses in the figure above, group the data.

There are two main types of unsupervised learning:

  • Clustering (grouping of the data)

  • Dimensionality reduction

Clustering

Clustering algorithms group the data into different categories based on ...