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HomeHomework Helpmachine-learningUnsupervised Learning

Unsupervised Learning

Unsupervised learning refers to a type of machine learning where the algorithm learns patterns and relationships in the data without prior knowledge of the expected output, often used for clustering and dimensionality reduction. This approach is essential when the data lacks labeled responses or when the goal is to discover hidden structures in the data.

intermediate
3 hours
Machine Learning
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Overview

Unsupervised learning is a powerful approach in machine learning that enables algorithms to analyze and interpret data without the need for labeled outputs. This method is particularly useful for discovering hidden patterns and structures within datasets, making it essential for exploratory data ana...

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Key Terms

Clustering
Grouping similar data points together based on certain characteristics.

Example: Customer segmentation based on purchasing behavior.

Dimensionality Reduction
The process of reducing the number of features in a dataset while retaining essential information.

Example: Using PCA to reduce a dataset from 100 features to 10.

Anomaly Detection
Identifying rare items or events that differ significantly from the majority of the data.

Example: Detecting fraudulent transactions in banking.

Association Rules
Rules that highlight relationships between variables in large datasets.

Example: If a customer buys bread, they are likely to buy butter.

K-means
A popular clustering algorithm that partitions data into K distinct clusters.

Example: Segmenting customers into 3 groups based on spending habits.

PCA
Principal Component Analysis, a technique for reducing dimensionality.

Example: Using PCA to visualize high-dimensional data in 2D.

Related Topics

Supervised Learning
A type of machine learning where models are trained on labeled data.
intermediate
Reinforcement Learning
A learning paradigm where agents learn by interacting with an environment.
advanced
Deep Learning
A subset of machine learning that uses neural networks for complex tasks.
advanced

Key Concepts

ClusteringDimensionality ReductionAnomaly DetectionAssociation Rules