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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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    تاریخ انتشار: 12 بهمن 1401
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