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Unsupervised learning is a category of machine learning where the algorithm is trained on input data without explicit supervision. Unlike supervised learning, where the algorithm is provided with labeled examples (input-output pairs) to learn from, unsupervised learning involves discovering patterns, relationships, and structures within the data without predefined outputs.

The primary goal of unsupervised learning is to explore the inherent structure present in the input data. It is often used for tasks such as clustering, dimensionality reduction, and density estimation. Here are some common types of unsupervised learning:

**Clustering**:

Clustering involves grouping similar data points together based on certain criteria or features. Algorithms like k-means clustering or hierarchical clustering are commonly used for this purpose.

**Dimensionality Reduction**:

Dimensionality reduction aims to reduce the number of features in the dataset while preserving its essential characteristics. Techniques like Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are examples of dimensionality reduction methods.

**Anomaly Detection**:

Anomaly detection, also known as outlier detection, involves identifying data points that deviate significantly from the norm. This can be useful in detecting rare events or unusual patterns in the data.

**Association Rule Learning**:

Association rule learning focuses on discovering interesting relationships or associations between variables in large datasets. Apriori and Eclat are examples of algorithms used for association rule learning.

**Generative Modeling**:

Generative modeling involves learning the underlying probability distribution of the data to generate new, similar samples. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are popular generative models.

Unsupervised learning is particularly valuable when dealing with unlabeled data, as it allows for exploration and discovery of patterns without the need for explicit target labels. The insights gained from unsupervised learning can be useful for subsequent supervised learning tasks or for better understanding the structure of the data.