Linear Regression: Basics

Related lectures (29)

Page 2 of 3

Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.

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

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Explores binary classification by regression, decision functions, and various cost functions.

Covers the basics of machine learning, including supervised and unsupervised learning, linear regression, and classification.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

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

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.