Unsupervised learning is a type of machine learning where the algorithm is trained on data without explicit instructions on what to do with the data. The algorithm must figure out what to do with the data on its own. It is a type of learning where you only have input data (X) and no corresponding output labels.
Here are some common types of unsupervised learning:
Clustering: This is where the algorithm tries to identify patterns in the data. It is used for grouping data into clusters.
- K-means: This is one of the most popular clustering algorithms. It works by assigning data points to one of K clusters based on the distance from the centroid of the cluster.
Dimensionality Reduction: This is the process of reducing the number of variables under consideration by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
- PCA (Principal Component Analysis): This is a technique used to reduce the dimensionality of large datasets, increasing interpretability while minimizing information loss.
Anomaly Detection: This is the process of identifying data points that do not conform to the expected behavior or pattern of the majority of the dataset.
Would you like to explore more about Machine Learning? Check out our Machine Learning Tutorial!
Here is an example of an image related to unsupervised learning: