Machine Learning Linear Regression aims to establish a linear relationship between a dependent variable (the outcome to be predicted) and one or more independent variables (the input features) to make accurate predictions.

The linear regression model is expressed as y = mx + b, where 'y' is the dependent variable, 'x' is the independent variable, 'm' is the slope of the line, and 'b' is the y-intercept.

The slope (m) in a linear regression model indicates the direction of the relationship between the independent and dependent variables. A positive slope indicates a positive relationship, while a negative slope indicates a negative relationship.

The y-intercept (b) in a linear regression model represents the value of the dependent variable when the independent variable is equal to zero.

The least squares method is an optimization technique used in linear regression to find the best-fit line that minimizes the sum of squared errors between the predicted values and the actual values of the dependent variable.

The coefficient of determination (R^2) in linear regression is a statistical measure that indicates the goodness of fit of the model. It represents the proportion of variance in the dependent variable that is explained by the independent variables.

Regularization is a technique used in linear regression to prevent overfitting, which occurs when the model learns the training data too well and starts to make poor predictions on new data. Regularization adds a penalty term to the loss function that discourages the model from fitting the training data too closely.

L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net regularization are all commonly used regularization techniques in linear regression. L1 regularization adds a penalty term to the sum of absolute coefficients, L2 regularization adds a penalty term to the sum of squared coefficients, and Elastic Net regularization is a combination of both L1 and L2 regularization.

Cross-validation is a technique used in linear regression to evaluate the performance of the model on unseen data. It involves dividing the data into multiple subsets, training the model on a subset and evaluating it on the remaining subsets, and then repeating this process multiple times to obtain an overall estimate of the model's performance.

K-fold cross-validation, leave-one-out cross-validation, and stratified cross-validation are all commonly used cross-validation techniques in linear regression. K-fold cross-validation divides the data into k subsets, leave-one-out cross-validation uses each data point as a test set once, and stratified cross-validation ensures that each fold contains a representative distribution of the target variable.

Feature scaling is a technique used in linear regression to transform the features to have a mean of 0 and a standard deviation of 1. This improves the numerical stability of the model and makes it less sensitive to the scale of the features.

Standard scaling, min-max scaling, and max-abs scaling are all commonly used feature scaling techniques in linear regression. Standard scaling transforms the features to have a mean of 0 and a standard deviation of 1, min-max scaling transforms the features to have a minimum value of 0 and a maximum value of 1, and max-abs scaling transforms the features to have a maximum absolute value of 1.

Outlier detection is a technique used in linear regression to identify and remove data points that may adversely affect the model's performance. Outliers can be caused by measurement errors, data entry errors, or simply the presence of extreme values in the data.

Z-score method, Grubbs' test, and isolation forest are all commonly used outlier detection techniques in linear regression. The Z-score method identifies outliers as data points that are more than a certain number of standard deviations away from the mean, Grubbs' test identifies outliers as data points that are significantly different from the rest of the data, and isolation forest identifies outliers as data points that are difficult to reach from the rest of the data.

Model selection is a technique used in linear regression to select the best model among a set of candidate models. The goal is to select the model that has the best generalization performance, which is the ability to make accurate predictions on new data.

Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and cross-validation are all commonly used model selection techniques in linear regression. AIC and BIC are information criteria that penalize models for their complexity, while cross-validation evaluates the performance of the model on unseen data.