Course Content

Mathematics for Data Analysis and Modeling

## Mathematics for Data Analysis and Modeling

# Challenge: Optimising Function Of Multiple Variables

Task

The most commonly used loss function in linear regression is the **Mean Squared Error (MSE)** loss function. This function is the **squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation**. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Task

The most commonly used loss function in linear regression is the **Mean Squared Error (MSE)** loss function. This function is the **squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation**. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Everything was clear?

# Challenge: Optimising Function Of Multiple Variables

Task

The most commonly used loss function in linear regression is the **Mean Squared Error (MSE)** loss function. This function is the **squared Euclidean distance between the variable's real value and the value we obtained using linear regression approximation**. Since this is a function of several variables, we can optimize it using gradient descent.

Your task is to use the optimization method to find the best parameters of the linear regression function:

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Task

**Mean Squared Error (MSE)** loss function. This function is the

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Everything was clear?

# Challenge: Optimising Function Of Multiple Variables

Task

**Mean Squared Error (MSE)** loss function. This function is the

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Task

**Mean Squared Error (MSE)** loss function. This function is the

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.

Everything was clear?

Task

**Mean Squared Error (MSE)** loss function. This function is the

- Create an
`initial_params`

variable that will store initial values for parameters of the linear regression function. - Provide minimization of the MSE function.
- Get the resulting optimal values of parameters.

Note

You can find more information about linear regression in Linear Regression with Python course.