Linear Regression: Basics and Applications

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

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

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

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

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

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

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Covers the analysis of various data attributes and linear regression models.

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

Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.