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Mathematics for Data Analysis and Modeling
course content

Course Content

Mathematics for Data Analysis and Modeling

Mathematics for Data Analysis and Modeling

1. Basic Mathematical Concepts and Definitions
What is a Function?What are Number Series?Sum of Elements of The SeriesChallenge: Calculating Sum of Geometric ProgressionVectors and Matrices
2. Linear Algebra
Numerical Operations on Vectors and MatricesChallenge: Calculate the Matrix Multiplication ResultMatrix DeterminantScaling Factor of the Linear TransformationChallenge: Figures' Linear TransformationsInversed and Transposed MatricesSystem of Linear EquationsChallenge: Solving the Task Using SLEEigenvalues and Eigenvectors
3. Mathematical Analysis
Derivative of the FunctionPartial Derivative of the FunctionChallenge: Solving Task Using DerivativeOptimization ProblemChallenge: Solving the Optimisation ProblemGradient Descent MethodChallenge: Optimising Function Of Multiple Variables

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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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?

Section 3. Chapter 7
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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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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?

Section 3. Chapter 7
toggle bottom row

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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. 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

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:

  1. Create an initial_params variable that will store initial values for parameters of the linear regression function.
  2. Provide minimization of the MSE function.
  3. Get the resulting optimal values of parameters.

Note

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

Section 3. Chapter 7
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