In this lecture, I learned about unsupervised learning. I’ve learned that it’s a machine learning method where algorithms explore data without predefined labels or outcomes. I understood that the main objective is to uncover patterns and structures within the data, primarily through clustering and dimensionality reduction techniques. I’ve gained the knowledge that unsupervised learning has numerous real-world applications, and that it plays a crucial role in discovering insights from data where the inherent structure isn’t immediately apparent.
I also learned about clustering in unsupervised learning. Clustering in unsupervised learning is the process of grouping similar data points together without predefined categories. The goal of clustering is to identify natural groupings or clusters within the dataset, allowing for the discovery of underlying structures and relationships. Once the clusters are formed, the results are interpreted by examining the characteristics of data points within each cluster. This can provide insights into the natural groupings or patterns present in the data. Various clustering algorithms, such as K-means and hierarchical clustering, are used to achieve this.