Regression: Simple and Multiple Linear

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Related lectures (31)

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Linear Regression: Fundamentals and Applications

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

Linear Regression: Regularization

Covers linear regression, regularization, and probabilistic models in generating labels.

Linear Regression Basics

Introduces the basics of linear regression, covering OLS approach, residuals, hat matrix, and Gauss-Markov assumptions.

Linear Regression: Concepts and Applications

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Linear Regression Essentials

Covers the essentials of linear regression, focusing on using multiple quantitative explanatory variables to predict a quantitative outcome.

Linear Regression and Logistic Regression

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

Linear Regression Model

Explores the linear regression model, OLS properties, hypothesis testing, interpretation, transformations, and practical considerations.

Linear Regression: Foundations and Applications

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

Regression II

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.