Definition: Unsupervised learning is a type of machine learning where the algorithm is not provided with labeled data.

Detailed Description:

In unsupervised learning, the algorithm must discover patterns and relationships within the data on its own. This can be used for tasks such as:

• Clustering: Grouping similar data points together.
• Dimensionality reduction: Reducing the number of features in a dataset.
• Anomaly detection: Identifying unusual data points.

Unsupervised learning algorithms include:

• K-means clustering
• Hierarchical clustering
• Principal Component Analysis (PCA)
• t-SNE
• Autoencoders

Related Terms:

• Machine Learning
• Clustering
• Dimensionality Reduction
• Anomaly Detection

FAQs:

1. What is the difference between supervised and unsupervised learning?

   • In supervised learning, the algorithm is provided with labeled data, while in unsupervised learning, the algorithm is not given any labels.

2. What are some applications of unsupervised learning?

   • Customer segmentation, image compression, and feature engineering.

3. What are the challenges of unsupervised learning?

   • Evaluating the performance of unsupervised learning algorithms can be difficult, as there are no ground truth labels.