Unsupervised Learning

Unsupervised learning is a machine learning technique that extracts patterns from unstructured or unlabeled data. Unlike supervised learning, unsupervised learning does not require labeled data, which makes it a more flexible approach.

Common Unsupervised Learning Techniques

• Clustering: groups similar data points.
• Dimensionality Reduction: reduces the number of variables in a dataset while retaining the important information.

Clustering Algorithms

• K-means: partitions data points into k clusters based on their similarity.
• Hierarchical Clustering: creates a tree-like structure of clusters.

Dimensionality Reduction Techniques

• Principal Component Analysis (PCA): finds the most important variables in a dataset.

Applications of Unsupervised Learning

• Customer Segmentation
• Anomaly Detection
• Image Processing

Unsupervised learning can be more challenging than supervised learning because there is no ground truth to evaluate the model's performance. Therefore, unsupervised learning often requires more exploratory analysis and experimentation to find the best approach.

All courses were automatically generated using OpenAI's GPT-3. Your feedback helps us improve as we cannot manually review every course. Thank you!