Clustering: Theory and Practice

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Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Machine Learning: Supervised and Unsupervised Learning Techniques

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

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.

Linear Dimensionality Reduction: PCA and LDA

Explores PCA and LDA for linear dimensionality reduction in data, emphasizing clustering and class separation techniques.

Neural Networks Recap: Activation Functions

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Introduction to Machine Learning: Linear Models

Introduces linear models for supervised learning, covering overfitting, regularization, and kernels, with applications in machine learning tasks.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Clustering Methods: K-means and DBSCAN

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