Building Regression Models with scikit-learn

سرفصل های دوره

This course covers important techniques such as ordinary least squares regression, moving on to lasso, ridge, and Elastic Net, and advanced techniques such as Support Vector Regression and Stochastic Gradient Descent Regression.

1. Course Overview

1. Course Overview

2. Understanding Linear Regression as a Machine Learning Problem

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Connecting the Dots with Linear Regression

05. Minimizing Least Square Error

06. Installing and Setting up scikit-learn

07. Exploring the Automobile Mpg Dataset

08. Visualizing Relationships and Correlations in Features

09. Mitigating Risks in Simple and Multiple Regression

10. R-squared and Adjusted R-squared

11. Regression with Categorical Variables

12. Module Summary

3. Building a Simple Linear Model

1. Module Overview

2. Simple Linear Regression

3. Linear Regression with Multiple Features

4. Standardizing Numeric Data

5. Label Encoding and One-hot Encoding Categorical Data

6. Linear Regression and the Dummy Trap

7. Module Summary

4. Building Regularized Regression Models

01. Module Overview

02. Overview of Regression Models in scikit-learn

03. Overfitting and Regularization

04. Lasso, Ridge and Elastic Net Regression

05. Defining Helper Functions to Build and Train Models and Compare Results

06. Single Feature, Kitchen Sink, and Parsimonious Linear Regression

07. Lasso Regression

08. Ridge Regression

09. Elastic Net Regression

10. Module Summary

5. Performing Regression Using Multiple Techniques

01. Module Overview

02. Choosing Regression Algorithms

03. Support Vector Regression

04. Implementing Support Vector Regression

05. Nearest Neighbors Regression

06. Implementing K-nearest-neighbors Regression

07. Stochastic Gradient Descent Regression

08. Implementing Stochastic Gradient Descent Regression

09. Decision Tree Regression

10. Implementing Decision Tree Regression

11. Least Angle Regression

12. Implementing Least Angle Regression

13. Regression with Polynomial Relationships

14. Module Summary

6. Hyperparameter Tuning for Regression Models

1. Module Overview

2. Hyperparameter Tuning

3. Hyperparameter Tuning for Lasso Regression Using Grid Search

4. Tuning Different Regression Models Using Grid Search

5. Summary and Further Study

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تولید کننده: PluralSight