Nonlinear ML Algorithms

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Related lectures (31)

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Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Overfitting, Cross-validation, Regularization

Explores overfitting, cross-validation, and regularization in machine learning, emphasizing model complexity and the importance of regularization strength.

Cross-validation & Regularization

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

Machine Learning Fundamentals: Regularization and Cross-validation

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Polynomial Regression: Basics and Regularization

Covers the basics of polynomial regression and regularization to prevent overfitting.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Overfitting, Cross-validation & Regularization

Explores model complexity, overfitting, and the role of cross-validation and regularization in machine learning.

Introduction to Machine Learning: Linear Models

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