Nearest Neighbor Classifier: Curse of Dimensionality

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Covers linear classification using logistic regression, regularization, and multiclass classification.

Covers the concept of kernel regression and K-nearest neighbors for making data linearly separable.

Introduces machine learning basics, performance metrics, optimization techniques, and model evaluation.

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

Covers the fundamentals of advanced machine learning, emphasizing practical applications through interactive exercises and projects.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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

Explores the applications and limitations of Principal Component Analysis, including denoising, compression, and regression.