Unravelling the Mystery of Unsupervised Learning
Unsupervised learning is a type of machine learning where the model is given unlabelled data and left to find patterns and relationships on its own. Unlike supervised learning, there are no predefined labels or outcomes. Instead, the algorithm explores the data, seeking hidden structures and insights.
Types of Unsupervised Learning:
Clustering and association are two fundamental concepts in unsupervised learning,
1. Clustering: Unveiling Similarities and Grouping Data
Clustering is a technique that involves grouping similar data points together based on certain features or characteristics. The primary goal is to uncover inherent structures within the data and organize it into clusters, where items within a cluster are more similar to each other than to those in other clusters.
Popular Clustering Algorithms: K-Means Clustering, Hierarchical Clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), etc..
Applications:
2. Association: Discovering Relationships and Patterns
Association, on the other hand, focuses on discovering interesting relationships or patterns among variables in a dataset. The primary goal is to identify associations, correlations, or dependencies between different attributes.
Popular Association Rule Mining Algorithms:
Applications:
In conclusion, unsupervised learning empowers machines to learn from data without explicit guidance, fostering a deeper understanding of underlying patterns and structures. As we wrap up Day 3 of our learning journey, stay tuned for more insights into the diverse landscape of machine learning.
Let's continue unravelling the mysteries together!