Basic Methods

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Linear Regression

The main aim is to estimate a linear equation representing the given set of data. There are two approaches to this.

1. A closed form solution.
   This can be directly obtained by solving the linear differential equation. Calculate the partial derivative of the error function wrt x, y and equate both of them to zero to get the values of the parameters which minimizes the error function.
2. An iterative approach.
   This is similar to Gradient Descent. We try to obtain the minima (L1, L2 norm etc) by calculating the gradient at each point and moving in small steps along the gradient vector. Refer to this video for more details.

Logistic Regression

Refer to the following link to see an example of logistic regression.