Introducing unsupervised learning
If the data is not generated randomly, it tends to exhibit certain patterns or relationships among its elements within a multi-dimensional space. Unsupervised learning involves the process of detecting and utilizing these patterns within a dataset to structure and comprehend it more effectively. Unsupervised learning algorithms uncover these patterns and use them as a foundation for imparting a certain structure to the dataset. The identification of these patterns contributes to a deeper understanding and representation of the data. Extracting patterns from raw data leads to a better understanding of the raw data.
This concept is shown in Figure 6.1:
Figure 6.1: Using unsupervised machine learning to extract patterns from unlabeled raw data
In the upcoming discussion, we will navigate through the CRISP-DM lifecycle, a popular model for the machine learning process. Within this context, we’ll pinpoint where unsupervised learning...
