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Machine Learning – Linear Regression

Artificial intelligence is going to be one of the biggest helper for us in the future or now. If you are interesting the Artificial Intelligence, we need the know some knowledges about the AI. To have knowledge about the AI, we have to know some knowledges about the Machine Learning. In this article, I am going to talk about the Linear Regression.

Linear Regression

Regression is making predict about the target value. Linear is making a line about the this target value. Linear Regression is making relationship between the two or more conditions and making predict about the values between these numbers or situations. These values usually independent from each other.

From this expression, y is predict about the number which you entered or try to analyzed, y_0 is the point which intersect with the y axis, y_1 is the slope of the this line and x is the value which you try to result of this value.

To understand how our predict value is verify the real result we have to use this formula. predi is the value which we want to have and yi is the substarction value between the real value and line predicted value. We have to take square root because the value should be positive and we sum all the values and divide by the all number which we try to observe.

I will share a article which will be about the Linear Regression examples with python. With the examples you will understand better. You can ask your questions via e-mail or comments

One Comment

  1. Gradient descent is often taught using a linear regression model because it is relatively straightforward to understand. In practice, it is useful when you have a very large dataset either in the number of rows or the number of columns that may not fit into memory.

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