Machine Learning With Mathematics | Simple Linear Regression | AI Asaan Hai

Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed. One of the most common techniques used in machine learning is simple linear regression, which involves fitting a line to a set of data points. This allows us to predict the value of one variable based on the value of another variable.

In simple linear regression, we are trying to find the relationship between two variables – the independent variable (x) and the dependent variable (y). The goal is to find a line of best fit that minimizes the sum of squared errors between the actual data points and the predicted values from the line.

The formula for a simple linear regression line is 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 and intercept are determined by minimizing the sum of squared errors using techniques like gradient descent.

Once we have the line of best fit, we can use it to make predictions for new data points. For example, if we have a set of data points that represent the relationship between temperature and ice cream sales, we can use simple linear regression to predict how many ice cream cones will be sold at a certain temperature.

Overall, simple linear regression is a powerful tool that allows us to uncover relationships between variables and make predictions based on those relationships. By combining machine learning with mathematics, we can unlock valuable insights and make informed decisions in a wide range of applications.