Linear Regression for ML

Kursusindhold

Linear Regression for ML

Linear Regression for ML

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building the Linear Regression with scikit-learn Challenge Cheatsheet

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Challenge Cheatsheet

3. Polynomial Regression

Quadratic Regression Polynomial Regression PolynomialFeatures Building the Polynomial Regression Interpolation vs Extrapolation Challenge Cheatsheet

4. Evaluating and Comparing Models

Cheatsheet

Congratulations on finishing the section!
Here is a cheatsheet with key takeaways.

You can download the image by simply clicking on it

Congratulations on learning Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression!
Next, you will learn how to evaluate the models' performance and compare them to choose the best one.
You will also learn about the problems you might face while using these models, such as overfitting, and how to address them.

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Tak for dine kommentarer!

Sektion 3. Kapitel 7

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Kursusindhold

Linear Regression for ML

Linear Regression for ML

1. Simple Linear Regression

What is Linear Regression Finding the Parameters Building the Linear Regression with scikit-learn Challenge Cheatsheet

2. Multiple Linear Regression

Linear Regression with Two Features Linear Regression with n Features Building Multiple Linear Regression Challenge Cheatsheet

3. Polynomial Regression

Quadratic Regression Polynomial Regression PolynomialFeatures Building the Polynomial Regression Interpolation vs Extrapolation Challenge Cheatsheet

4. Evaluating and Comparing Models

Cheatsheet

Congratulations on finishing the section!
Here is a cheatsheet with key takeaways.

You can download the image by simply clicking on it

Congratulations on learning Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression!
Next, you will learn how to evaluate the models' performance and compare them to choose the best one.
You will also learn about the problems you might face while using these models, such as overfitting, and how to address them.

Var alt klart?

Tak for dine kommentarer!

Sektion 3. Kapitel 7

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