(x(1), y(1)) = 1, 1.5, (x(2), y(2)) = 2, 3, (x(3), y(3)) = 3, 4.5. Hypothesis: h(x) = t1x, where t1 = 2. How much error is obtained?

Machine Learning quiz on linear regression in machine learning, linear regression cost functions, and gradient descent. Linear regression is a statistical method and supervised machine learning algorithm that predicts continuous or quantitative values. It's one of the most widely used algorithms in machine learning problems.  Linear regression uses a linear equation to find the relationship between a dependent variable and one or more independent variables. The model predicts that each increment of a feature that is variable by some fixed amount increases or decreases the predicted output... Show more

Machine Learning quiz on linear regression in machine learning, linear regression cost functions, and gradient descent.

Linear regression is a statistical method and supervised machine learning algorithm that predicts continuous or quantitative values. It's one of the most widely used algorithms in machine learning problems. 

Linear regression uses a linear equation to find the relationship between a dependent variable and one or more independent variables. The model predicts that each increment of a feature that is variable by some fixed amount increases or decreases the predicted output by the same amount. This amount can be different for each feature. 

Linear regression is used to answer questions like: Predict future prices/costs, Predict future revenue, and Compare performance. 
Linear regression is advantageous when at least two variables are available in the data. It's used in a variety of business applications, such as stock market forecasting, portfolio management, and scientific analysis. 

Linear regression supports two methods for fitting a regression model:
Ordinary least squares: This method is best for small datasets and should give similar results to Excel.
Online gradient descent: This method is better for more complex models or models with too little training data. 

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