Supervised Learning with kNN: Regression Model

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Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Machine Learning Fundamentals

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

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Linear Regression Basics

Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.

Kernel Regression: K-nearest Neighbors

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

Examples of decision models for Supervised learning

Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.

Linear Regression: Basics and Applications

Covers the basics of linear regression in machine learning, exploring its applications in predicting outcomes like birth weight and analyzing relationships between variables.

Introduction to Machine Learning: Linear Models

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

Principal Component Analysis: Dimensionality Reduction

Covers Principal Component Analysis for dimensionality reduction, exploring its applications, limitations, and importance of choosing the right components.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.