Linear Regression
Linear regression is a fundamental statistical and machine learning technique used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. It assumes a linear relationship and aims to find the best-fitting straight line (or hyperplane in multiple dimensions) that minimizes the sum of squared differences between observed and predicted values. This method is widely applied for prediction, forecasting, and understanding correlations in fields like economics, biology, and data science.
Developers should learn linear regression as it serves as a foundational building block for understanding more complex machine learning algorithms and statistical modeling, making it essential for data analysis, predictive analytics, and AI applications. It is particularly useful in scenarios such as predicting sales based on advertising spend, estimating housing prices from features like size and location, or analyzing trends in time-series data, providing interpretable results that help in decision-making and hypothesis testing.