Unsupervised Machine Learning: Clustering Basics

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Clustering: K-means & LDA

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

Unsupervised Learning: Clustering & Dimensionality Reduction

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

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Clustering Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Machine Learning: Fundamentals and Applications

Introduces machine learning basics, covering data segmentation, clustering, classification, and practical applications like image classification and face similarity.

Unsupervised Learning: Dimensionality Reduction and Clustering

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Nearest Neighbor Rules: Part 2

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

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Introduction to Clustering: Methods and Applications

Covers the fundamentals of clustering in unsupervised learning and its practical applications.