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Data Science in Python: Classification Modeling

سرفصل های دوره

Learn Python for Data Science & Supervised Machine Learning, and build classification models with fun, hands-on projects


1. Introduction
  • 1. Course Introduction
  • 2. About This Series
  • 3. Course Structure & Outline
  • 4. Course Structure & Outline
  • 5. READ ME Important Notes for New Students.html
  • 6.1 Data Science in Python - Classification.pdf
  • 6.2 Data Science in Python - Classification.zip
  • 6. DOWNLOAD Course Resources.html
  • 7. Introducing the Course Project
  • 8. Setting Expectations
  • 9. Jupyter Installation & Launch

  • 2. Intro to Data Science
  • 1. What is Data Science
  • 2. The Data Science Skillset
  • 3. What is Machine Learning
  • 4. Common Machine Learning Algorithms
  • 5. Data Science Workflow
  • 6. Data Prep & EDA Steps
  • 7. Modeling Steps
  • 8. Classification Modeling
  • 9. Key Takeaways
  • 10. Intro to Data Science.html

  • 3. Classification 101
  • 1. Classification 101
  • 2. Goals of Classification
  • 3. Types of Classification
  • 4. Classification Modeling Workflow
  • 5. Key Takeaways
  • 6. Classification 101.html

  • 4. Data Prep & EDA
  • 1. EDA For Classification
  • 2. Defining a Target
  • 3. DEMO Defining a Target
  • 4. Exploring the Target
  • 5. Exploring the Features
  • 6. DEMO Exploring the Features
  • 7. ASSIGNMENT Exploring the Target & Features
  • 8. SOLUTION Exploring the Target & Features
  • 9. Correlation
  • 10. PRO TIP Correlation Matrix
  • 11. DEMO Correlation Matrix
  • 12. Feature-Target Relationships
  • 13. Feature-Feature Relationships
  • 14. PRO TIP Pair Plots
  • 15. ASSIGNMENT Exploring Relationships
  • 16. SOLUTION Exploring Relationships
  • 17. Feature Engineering Overview
  • 18. Numeric Feature Engineering
  • 19. Dummy Variables
  • 20. Binning Categories
  • 21. DEMO Feature Engineering
  • 22. Data Splitting
  • 23. Preparing Data for Modeling
  • 24. ASSIGNMENT Preparing the Data for Modeling
  • 25. SOLUTION Prepare the Data for Modeling
  • 26. Key Takeaways
  • 27. Data Prep & EDA.html

  • 5. K-Nearest Neighbors
  • 1. K-Nearest Neighbors
  • 2. The KNN Workflow
  • 3. KNN in Python
  • 4. Model Accuracy
  • 5. Confusion Matrix
  • 6. DEMO Confusion Matrix
  • 7. ASSIGNMENT Fitting a Simple KNN Model
  • 8. SOLUTION Fitting a Simple KNN Model
  • 9. Hyperparameter Tuning
  • 10. Overfitting & Validation
  • 11. DEMO Hyperparameter Tuning
  • 12. Hard vs. Soft Classification
  • 13. DEMO Probability vs. Event Rate
  • 14. ASSIGNMENT Tuning a KNN Model
  • 15. SOLUTION Tuning a KNN Model
  • 16. Pros & Cons of KNN
  • 17. Key Takeaways
  • 18. K-Nearest Neighbors.html

  • 6. Logistic Regression
  • 1. Logistic Regression
  • 2. Logistic vs. Linear Regression
  • 3. The Logistic Function
  • 4. Likelihood
  • 5. Multiple Logistic Regression
  • 6. The Logistic Regression Workflow
  • 7. Logistic Regression in Python
  • 8. Interpreting Coefficients
  • 9. ASSIGNMENT Logistic Regression
  • 10. SOLUTION Logistic Regression
  • 11. Feature Engineering & Selection
  • 12. Regularization
  • 13. Tuning a Regularized Model
  • 14. DEMO Regularized Logistic Regression
  • 15. ASSIGNMENT Regularized Logistic Regression
  • 16. SOLUTION Regularized Logistic Regression
  • 17. Multi-class Logistic Regression
  • 18. ASSIGNMENT Multi-class Logistic Regression
  • 19. SOLUTION Multi-class Logistic Regression
  • 20. Pros & Cons of Logistic Regression
  • 21. Key Takeaways
  • 22. Logistic Regression.html

  • 7. Classification Metrics
  • 1. Classification Metrics
  • 2. Accuracy, Precision & Recall
  • 3. DEMO Accuracy, Precision & Recall
  • 4. PRO TIP F1 Score
  • 5. ASSIGNMENT Model Metrics
  • 6. SOLUTION Model Metrics
  • 7. Soft Classification
  • 8. DEMO Leveraging Soft Classification
  • 9. PRO TIP Precision-Recall & F1 Curves
  • 10. DEMO Plotting Precision-Recall & F1 Curves
  • 11. The ROC Curve & AUC
  • 12. DEMO The ROC Curve & AUC
  • 13. Classification Metrics Recap
  • 14. ASSIGNMENT Threshold Shifting
  • 15. SOLUTION Threshold Shifting
  • 16. Multi-class Metrics
  • 17. Multi-class Metrics in Python
  • 18. ASSIGNMENT Multi-class Metrics
  • 19. SOLUTION Multi-class Metrics
  • 20. Key Takeaways
  • 21. Classification Metrics.html

  • 8. Imbalanced Data
  • 1. Imbalanced Data
  • 2. Managing Imbalanced Data
  • 3. Threshold Shifting
  • 4. Sampling Strategies
  • 5. Oversampling
  • 6. Oversampling in Python
  • 7. DEMO Oversampling
  • 8. SMOTE
  • 9. SMOTE in Python
  • 10. Undersampling
  • 11. Undersampling in Python
  • 12. ASSIGNMENT Sampling Methods
  • 13. SOLUTION Sampling Methods
  • 14. Changing Class Weights
  • 15. DEMO Changing Class Weights
  • 16. ASSIGNMENT Changing Class Weights
  • 17. SOLUTION Changing Class Weights
  • 18. Imbalanced Data Recap
  • 19. Key Takeaways
  • 20. Imbalanced Data.html

  • 9. Mid-Course Project
  • 1. Project Brief
  • 2. Solution Walkthrough

  • 10. Decision Trees
  • 1. Decision Trees
  • 2. Entropy
  • 3. Decision Tree Predictions
  • 4. Decision Trees in Python
  • 5. DEMO Decision Trees
  • 6. Feature Importance
  • 7. ASSIGNMENT Decision Trees
  • 8. SOLUTION Decision Trees
  • 9. Hyperparameter Tuning for Decision Trees
  • 10. DEMO Hyperparameter Tuning
  • 11. ASSIGNMENT Tuned Decision Tree
  • 12. SOLUTION Tuned Decision Tree
  • 13. Pros & Cons of Decision Trees
  • 14. Key Takeaways
  • 15. Decision Trees.html

  • 11. Ensemble Models
  • 1. Ensemble Models
  • 2. Simple Ensemble Models
  • 3. DEMO Simple Ensemble Models
  • 4. ASSIGNMENT Simple Ensemble Models
  • 5. SOLUTION Simple Ensemble Models
  • 6. Random Forests
  • 7. Fitting Random Forests in Python
  • 8. Hyperparameter Tuning for Random Forests
  • 9. PRO TIP Random Search
  • 10. Pros & Cons of Random Forests
  • 11. ASSIGNMENT Random Forests
  • 12. SOLUTION Random Forests
  • 13. Gradient Boosting
  • 14. Gradient Boosting in Python
  • 15. Hyperparameter Tuning for Gradient Boosting
  • 16. DEMO Hyperparameter Tuning for Gradient Boosting
  • 17. Pros & Cons of Gradient Boosting
  • 18. ASSIGNMENT Gradient Boosting
  • 19. SOLUTION Gradient Boosting
  • 20. PRO TIP SHAP Values
  • 21. DEMO SHAP Values
  • 22. Key Takeaways
  • 23. DEMO Ensemble Models.html
  • 24. Ensemble Models.html

  • 12. Classification Summary
  • 1. Recap Classification Models & Workflow
  • 2. Pros & Cons of Classification Models
  • 3. DEMO Production Pipeline & Deployment
  • 4. Looking Ahead Unsupervised Learning

  • 13. Final Project
  • 1. Project Brief
  • 2. Solution Walkthrough

  • 14. Next Steps
  • 1. BONUS LESSON.html
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    تاریخ انتشار: ۲۸ فروردین ۱۴۰۳
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