Unsupervised Machine Learning

UnSupervised Machine Learning is a type of Machine Learning where the model is trained on data without labeled responses. The algorithm tries to learn the underlying structure of the data by identifying patterns and relationships without explicit Guidance on what to predict. This approach is often used for clustering, association, and dimensionality reduction tasks.

Examples of unSupervised Machine Learning include:

• K-Means Clustering: Grouping similar data points into clusters, such as segmenting customers based on purchasing behavior.
• Hierarchical Clustering: Creating a tree of clusters to identify nested groupings, like organizing documents by topic.
• Principal Component Analysis (PCA): Reducing the dimensionality of data while preserving variance, often used in image processing.
• Association Rule Learning: Discovering interesting relations between variables in large databases, such as market basket analysis to find items frequently bought together.

Use cases include:

• Customer Segmentation: Identifying distinct groups within a customer base to tailor Marketing strategies.
• Anomaly Detection: Detecting unusual patterns in data, useful for fraud detection in finance.
• Image Compression: Reducing the size of images by identifying and retaining essential features.
• Genetic Data Analysis: Finding patterns in genetic data to identify potential disease markers.