Building Classification Models with scikit-learn

سرفصل های دوره

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.

1. Course Overview

1. Course Overview

2. Understanding Classification as a Machine Learning Problem

01. Version Check

02. Module Overview

03. Prerequisites and Course Outline

04. Classification as a Machine Learning Problem

05. Logistic Regression Intuition

06. Cross Entropy Intuition

07. Accuracy, Precision, and Recall

08. Determining Decision Threshold Using ROC Curves

09. Types of Classification

10. Module Summary

3. Building a Simple Classification Model

1. Module Overview

2. Installing and Setting up scikit-learn

3. Exploring the Titanic Dataset

4. Visualizing Relationships in the Data

5. Preprocessing the Data

6. Training a Logistic Regression Binary Classifier

7. Calculating Accuracy, Precision and Recall for the Classification Model

8. Defining Helper Functions to Train and Evaluate Classification Models

9. Module Summary

4. Performing Classification Using Multiple Techniques

01. Module Overview

02. Choosing Classification Algorithms

03. Linear Discriminant Analysis and Quadratic Discriminant Analysis

04. Implementing Linear Discriminant Analysis Classification

05. Implementing Quadratic Discriminant Analysis Classification

06. Stochastic Gradient Descent

07. Implementing Stochastic Gradient Descent Classification

08. Support Vector Machines

09. Implementing Support Vector Classification

10. Nearest Neighbors

11. Implementing K-nearest-neighbors Classification

12. Decision Trees

13. Implementing Decision Tree Classification

14. Naive Bayes

15. Implementing Naive Bayes Classification

16. Module Summary

5. Hyperparameter Tuning for Classification Models

1. Module Overview

2. Hyperparameter Tuning

3. Hyperparameter Tuning a Decision Tree Clasifier Using Grid Search

4. Hyperparameter Tuning a Logistic Regression Classifier Using Grid Search

5. Module Summary

6. Applying Classification Models to Images

1. Module Overview

2. Representing Images as Matrices

3. Exploring the Fashion MNIST Dataset

4. Classifying Images Using Logistic Regression

5. Summary and Further Study

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تولید کننده: PluralSight