Introduction to Machine Learning: Basics and Examples

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

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

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explores models, learning paradigms, and applications in Mathematics of Data.

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

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

Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.

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

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

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