K-Means Algorithm: Implementation and Application as Recommendation System

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Unsupervised Learning: PCA & K-means

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.

Unsupervised Behavior Clustering

Explores unsupervised behavior clustering and dimensionality reduction techniques, covering algorithms like K-Means, DBSCAN, and Gaussian Mixture Model.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Clustering & Density Estimation

Covers dimensionality reduction, clustering, and density estimation techniques, including PCA, K-means, GMM, and Mean Shift.

Clustering Methods: K-means and DBSCAN

Explores K-means and DBSCAN clustering methods, discussing properties, drawbacks, initialization, and optimal cluster selection.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

K-means Algorithm

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.