K-means Clustering: Assignment and Update Steps

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Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Dimensionality Reduction: PCA and LDA

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

Clustering & Density Estimation

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Clustering Methods

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

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.

Unsupervised Learning: Clustering Methods

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.