Practical Data Science using Python - Unsupervised Learning - K-Means Clustering

The video tutorial introduces K-Means clustering, a popular unsupervised learning algorithm. It explains the difference between supervised and unsupervised learning, providing examples like customer segmentation and document classification. The tutorial also covers the concept of data dimensions and how K-Means clustering groups similar data points to discover patterns. The process of clustering in multi-dimensional data is discussed, emphasizing the importance of determining the number of clusters (K) before applying the algorithm.

Can you provide an example of how K-means clustering can be used for customer segmentation?

In what scenarios would unsupervised learning be preferred over supervised learning?

What challenges might arise when clustering high-dimensional data?

What is the significance of the number 'K' in K-means clustering?