Linear Regression: Basics

Related lectures (29)

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

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Covers the basics of linear regression, from training to real-world applications and multi-output scenarios.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

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

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

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

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

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

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