K-Means Clustering is an unsupervised learning algorithm that groups data points into clusters based on their similarity.

The goal of unsupervised learning is to find patterns and structures in data without being given labeled data.

Clustering is a common unsupervised learning task that involves grouping data points into clusters based on their similarity.

Hard clustering assigns each data point to a single cluster, while soft clustering allows a data point to belong to multiple clusters with different degrees of membership.

Euclidean distance is a common distance metric used in clustering algorithms that measures the distance between two data points in multidimensional space.

Dimensionality reduction is used in unsupervised learning to reduce the number of features in a dataset, improve the performance of clustering algorithms, and visualize high-dimensional data.

Principal Component Analysis (PCA) is a common dimensionality reduction technique that finds the directions of maximum variance in the data and projects the data onto these directions.

The goal of anomaly detection in unsupervised learning is to identify data points that are significantly different from the rest of the data.

Isolation Forest is a common anomaly detection algorithm that isolates data points by randomly selecting features and splitting the data into two subsets.

Supervised learning uses labeled data, while unsupervised learning uses unlabeled data. Supervised learning is used for classification and regression tasks, while unsupervised learning is used for clustering and dimensionality reduction tasks. Supervised learning is typically more accurate than unsupervised learning.

Gaussian Mixture Model (GMM) is an example of a generative unsupervised learning algorithm that assumes that the data is generated from a mixture of Gaussian distributions.

The goal of semi-supervised learning is to learn from a combination of labeled and unlabeled data.

Self-Training is a common semi-supervised learning algorithm that iteratively trains a model on labeled data and then uses the model to label unlabeled data.

The challenges of unsupervised learning include the lack of labeled data, the difficulty in finding meaningful patterns and structures in data, and the high computational cost of unsupervised learning algorithms.

Unsupervised learning has a wide range of applications, including customer segmentation, fraud detection, image clustering, natural language processing, and many more.