UNSUPERVISED MACHINE LEARNING

Unsupervised Machine Learning and How It Works

**Unsupervised Machine Learning (ML) **is a type of machine learning where a computer learns from data without being given labeled answers. Unlike supervised learning, where the model is trained using data with known outputs, unsupervised learning allows the system to find patterns, relationships, and structures in the data on its own.

How Unsupervised Learning Works

The process begins by providing the machine learning algorithm with a large amount of unlabeled data. The algorithm examines the data and identifies similarities, differences, and hidden patterns. It then groups or organizes the data based on these patterns.

• Another technique is dimensionality reduction, which reduces the number of features in a dataset while preserving important information. This can make large and complex datasets easier to analyze and visualize.

Applications of Unsupervised Learning

Unsupervised machine learning is used in many areas, including:

• Customer segmentation – grouping customers based on their behavior.
• Fraud detection – identifying unusual patterns in financial transactions.
• Recommendation systems – finding similarities between users or products.
• Data analysis – discovering hidden patterns in large datasets.
• image and document organization – grouping similar images or documents.

Conclusion

Unsupervised machine learning helps computers discover useful patterns in data without requiring humans to provide labeled answers. By using techniques such as clustering and dimensionality reduction, it can reveal hidden information and support decision-making in many different fields.