6. Unsupervised Learning – Clustering & Dimensionality Reduction

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6

Unsupervised Learning – Clustering & Dimensionality Reduction

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Unsupervised learning seeks to identify hidden patterns in unlabeled data,...
6.1

Clustering

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Clustering is the process of grouping similar data points together in...
6.1.1

What Is Clustering?

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Clustering is the process of grouping similar data points into clusters...
6.1.2

Types Of Clustering Algorithms

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This section discusses various clustering algorithms used in unsupervised...
6.1.2.1

K-Means Clustering

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K-Means Clustering is a centroid-based algorithm that partitions a dataset...
6.1.2.2

Hierarchical Clustering

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Hierarchical clustering is a technique that organizes data points into a...
6.1.2.3

Dbscan (Density-Based Spatial Clustering Of Applications With Noise)

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DBSCAN is a clustering algorithm that groups data points based on their...
6.1.3

Cluster Evaluation Metrics

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Cluster evaluation metrics help assess the quality of clustering algorithms.
6.2

Dimensionality Reduction

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Dimensionality Reduction techniques are used to simplify datasets by...
6.2.1

Why Reduce Dimensions?

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Reducing dimensions in data sets helps mitigate issues such as the curse of...
6.2.2

Principal Component Analysis (Pca)

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Principal Component Analysis (PCA) is a linear transformation technique that...
6.2.3

T-Sne (T-Distributed Stochastic Neighbor Embedding)

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t-SNE is a non-linear dimensionality reduction technique used for...
6.2.4

Umap (Uniform Manifold Approximation And Projection)

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UMAP is an innovative technique for dimensionality reduction that maintains...
6.3

Applications Of Clustering & Dimensionality Reduction

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Clustering and dimensionality reduction are vital techniques in unsupervised...
6.4

Chapter Summary

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The chapter presents an overview of unsupervised learning, focusing on...