Unsupervised Learning Tutorial

Unsupervised learning is a type of machine learning where the algorithm is given a dataset without any labels. The goal of the algorithm is to find patterns in the data. This tutorial will introduce you to the basics of unsupervised learning, including common algorithms and their applications.

Common Unsupervised Learning Algorithms

• Clustering: Grouping data into clusters so that data points within a cluster are more similar to each other than to those in other clusters.

  • K-Means
  • Hierarchical Clustering
  • DBSCAN

• Dimensionality Reduction: Reducing the number of variables in a dataset while retaining as much information as possible.

  • Principal Component Analysis (PCA)
  • t-SNE

• Association Rules: Finding interesting relationships between variables in large databases.

  • Apriori Algorithm
  • Eclat Algorithm

Applications of Unsupervised Learning

• Market Basket Analysis: Analyzing customer purchase data to identify patterns and trends.
• Image Compression: Reducing the size of images while retaining quality.
• Anomaly Detection: Identifying unusual patterns that may indicate fraud or errors.

Clustering

For more information on unsupervised learning, check out our Advanced Unsupervised Learning Tutorial.