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Lecture
Segmentation Techniques
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Related lectures (31)
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Feature Extraction & Clustering Methods
Covers feature extraction, clustering, and classification methods for high-dimensional datasets and behavioral analysis using PCA, t-SNE, k-means, GMM, and various classification algorithms.
Unsupervised Learning: Clustering & Dimensionality Reduction
Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.
K-means Clustering: Lloyd's Algorithm and RGB Space
Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.
Dimensionality Reduction: PCA & t-SNE
Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.
Unsupervised Learning: Clustering
Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.
Dimensionality Reduction: PCA and LDA
Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.
Machine Learning Fundamentals
Covers key concepts and examples of machine learning algorithms and techniques.
Clustering: Unsupervised Learning
Explores dimensionality reduction, clustering algorithms, and the state of machine learning.
Machine Learning: Supervised and Unsupervised Learning Techniques
Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Clustering: Unsupervised Learning
Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.
