Udemy - Data Mining for Business Analytics & Data Analysis in Python (3.2025)

磁力链接/BT种子名称

Udemy - Data Mining for Business Analytics & Data Analysis in Python (3.2025)

磁力链接/BT种子简介

种子哈希：b6865e5232f1388346b7273668bac29f6cbee612
文件大小： 2.1G
已经下载：117次
下载速度：极快
收录时间：2025-07-30
最近下载：2025-10-10

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磁力链接

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文件列表

  • 06. Dimension Reduction/17. Python - Challenge Solutions.mp4 92.8 MB
  • 04. CHAID/16. Python - Data Visualization with CHAID Model.mp4 83.5 MB
  • 10. XGBoost and SHAP/24. Python - Challenge Solutions.mp4 68.0 MB
  • 07. Association Rule Learning/12. Python - Challenge Solutions.mp4 62.0 MB
  • 09. LIME - Explainable Artificial Intelligence/3. Python - Preparing LIME.mp4 57.4 MB
  • 04. CHAID/18. Python - Challenge solutions.mp4 54.2 MB
  • 09. LIME - Explainable Artificial Intelligence/6. Python - Challenge Solutions.mp4 53.2 MB
  • 02. Survival Analysis/1. Game Plan for Survival Analysis section.mp4 51.9 MB
  • 04. CHAID/17. Extra Resources and Challenge.mp4 48.8 MB
  • 03. Cox Proportional Hazard Regression/4. Python - Preparing Script and Data.mp4 48.3 MB
  • 02. Survival Analysis/20. Python - Survival Analysis Challenge Solutions.mp4 39.2 MB
  • 03. Cox Proportional Hazard Regression/8. Python - Solution Challenges.mp4 39.1 MB
  • 06. Dimension Reduction/12. Python - PCA interpretation.mp4 38.8 MB
  • 08. Random Forest and Feature Selection/14. Python - Challenge Solutions.mp4 35.0 MB
  • 08. Random Forest and Feature Selection/13. Extra Resources and Challenge.mp4 34.3 MB
  • 03. Cox Proportional Hazard Regression/5. Python - Cox Proportional Hazard.mp4 33.0 MB
  • 02. Survival Analysis/11. Python - Calculating Specific Events.mp4 32.3 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/7. Python - Optimal Clusters.mp4 31.9 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/11. Python - Cluster Interpretation.mp4 30.2 MB
  • 07. Association Rule Learning/6. Python - Create Transaction List.mp4 29.7 MB
  • 01. Introduction/4. Diogo's Introduction and Background.mp4 29.4 MB
  • 07. Association Rule Learning/10. Python - Apriori Visualization.mp4 29.3 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/13. Python - Challenge solutions.mp4 29.2 MB
  • 10. XGBoost and SHAP/20. Python - Local Interpretability.mp4 28.4 MB
  • 03. Cox Proportional Hazard Regression/7. Extra Resources and Challenge.mp4 26.9 MB
  • 04. CHAID/15. Python - CHAID Model.mp4 26.8 MB
  • 01. Introduction/1. Introduction to Data Mining course for Business Analytics & Data Analysis.mp4 24.9 MB
  • 09. LIME - Explainable Artificial Intelligence/5. Extra Resources and Challenge.mp4 24.2 MB
  • 07. Association Rule Learning/7. Python - Encoding Transactions.mp4 23.8 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/12. Extra Resources and Challenge.mp4 22.9 MB
  • 02. Survival Analysis/19. Extra Resources and Survival Analysis Challenge.mp4 22.9 MB
  • 08. Random Forest and Feature Selection/7. Python - Training and Test Set.mp4 21.4 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/10. Python - Probability of belonging to each cluster.mp4 21.3 MB
  • 06. Dimension Reduction/16. Extra Resources and Challenge.mp4 21.1 MB
  • 07. Association Rule Learning/11. Extra Resources and Challenge.mp4 20.3 MB
  • 07. Association Rule Learning/9. Python - Association Rule Learning.mp4 19.7 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/9. Python - Cluster Prediction.mp4 19.5 MB
  • 08. Random Forest and Feature Selection/11. Python - Classification Report.mp4 18.9 MB
  • 06. Dimension Reduction/15. Python -Visualizing Manifold Learning.mp4 17.3 MB
  • 10. XGBoost and SHAP/23. Extra Resources and Challenge.mp4 17.3 MB
  • 04. CHAID/5. Python - Importing Libraries and Data.mp4 16.8 MB
  • 06. Dimension Reduction/13. Manifold Learning and t-SNE.mp4 16.7 MB
  • 02. Survival Analysis/2. Survival Analyisis Introduction.mp4 16.3 MB
  • 10. XGBoost and SHAP/4. Python - Loading Data.mp4 16.1 MB
  • 04. CHAID/10. Python - Transforming Jobs Variable.mp4 15.9 MB
  • 09. LIME - Explainable Artificial Intelligence/4. Python - Explaining Predictions.mp4 15.7 MB
  • 04. CHAID/12. Python - Transform Minimum Variable.mp4 15.2 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/2. Case Study Briefing and Clustering.mp4 15.0 MB
  • 02. Survival Analysis/6. Python - Loading Data.mp4 14.5 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/14. Will you help me.mp4 14.4 MB
  • 04. CHAID/4. Python - Installing libraries.mp4 14.3 MB
  • 08. Random Forest and Feature Selection/12. Python .- Feature Importance for Business Analytics.mp4 14.1 MB
  • 10. XGBoost and SHAP/14. Python - XGBoost Model.mp4 14.1 MB
  • 06. Dimension Reduction/9. Python - Optimal Number of Components.mp4 13.3 MB
  • 10. XGBoost and SHAP/17. Python - MAE and RSME.mp4 13.2 MB
  • 08. Random Forest and Feature Selection/9. Confusion Matrix, AUC, and F1-Score.mp4 13.2 MB
  • 02. Survival Analysis/7. Python - Transforming Dependent Variable.mp4 12.7 MB
  • 02. Survival Analysis/13. Python - Plotting Cumulative Curves.mp4 12.5 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/3. Gaussian Mixture Model vs. Kmeans.mp4 12.2 MB
  • 04. CHAID/11. Python - Transforming Experience Variable.mp4 12.2 MB
  • 10. XGBoost and SHAP/6. How XGBoost works part 1.mp4 12.2 MB
  • 04. CHAID/8. Python - Removing column and unique values check.mp4 12.0 MB
  • 06. Dimension Reduction/7. Python - Correlation Matrix.mp4 11.9 MB
  • 10. XGBoost and SHAP/21. Python - Dependency Plots.mp4 11.8 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/5. Python - Loading Data.mp4 11.7 MB
  • 02. Survival Analysis/17. Python - Plotting both Survival Curves.mp4 11.6 MB
  • 01. Introduction/3. Course Resources, Material, and Colab setup - Important!.mp4 11.3 MB
  • 02. Survival Analysis/4. Python - Changing Directory.mp4 11.1 MB
  • 08. Random Forest and Feature Selection/6. Random Forest.mp4 10.7 MB
  • 02. Survival Analysis/16. Python - Kaplan-Meyer Estimator per Gender.mp4 10.4 MB
  • 04. CHAID/7. CHAID Statistics and Quirks.mp4 10.2 MB
  • 06. Dimension Reduction/2. What is Dimension Reduction.mp4 9.9 MB
  • 09. LIME - Explainable Artificial Intelligence/2. LIME.mp4 9.8 MB
  • 07. Association Rule Learning/5. Association Rule Learning.mp4 9.6 MB
  • 02. Survival Analysis/18. Python - Log Rank Test.mp4 9.2 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/4. Python - Changing Directory and Importing Libraries.mp4 8.8 MB
  • 02. Survival Analysis/5. Python - Importing Libraries.mp4 8.8 MB
  • 02. Survival Analysis/10. Python - Kaplan-Meyer Estimator.mp4 8.8 MB
  • 10. XGBoost and SHAP/18. SHAP.mp4 8.6 MB
  • 06. Dimension Reduction/11. Python - PCA.mp4 8.6 MB
  • 08. Random Forest and Feature Selection/10. Python - Random Forest Predictions.mp4 8.3 MB
  • 07. Association Rule Learning/3. Python - Importing Libraries.mp4 8.2 MB
  • 04. CHAID/6. Introducing CHAID.mp4 8.2 MB
  • 06. Dimension Reduction/4. Python - Importing Libraries.mp4 8.2 MB
  • 08. Random Forest and Feature Selection/3. Python - Importing Libraries.mp4 8.1 MB
  • 08. Random Forest and Feature Selection/8. Python - Random Forest.mp4 8.0 MB
  • 06. Dimension Reduction/8. Python - Standardizing Variables.mp4 8.0 MB
  • 04. CHAID/1. Game Plan.mp4 7.8 MB
  • 06. Dimension Reduction/5. Python - Loading Data.mp4 7.6 MB
  • 10. XGBoost and SHAP/13. Python - XGBoost Parameters.mp4 7.5 MB
  • 08. Random Forest and Feature Selection/4. Python - Loading Data.mp4 7.3 MB
  • 10. XGBoost and SHAP/12. XGBoost Parameters.mp4 7.0 MB
  • 06. Dimension Reduction/3. Principal Component Analysis.mp4 6.9 MB
  • 08. Random Forest and Feature Selection/2. Case Study Briefing and Step by Step Guide.mp4 6.8 MB
  • 03. Cox Proportional Hazard Regression/2. Cox Proportional Hazard Regression.mp4 6.7 MB
  • 06. Dimension Reduction/14. Python - t-SNE.mp4 6.7 MB
  • 07. Association Rule Learning/4. Python - Loading Data.mp4 6.4 MB
  • 10. XGBoost and SHAP/7. How XGBoost works part 2.mp4 6.4 MB
  • 06. Dimension Reduction/6. Python - Transforming String Variables.mp4 6.4 MB
  • 04. CHAID/3. Problem Statement.mp4 6.3 MB
  • 02. Survival Analysis/9. Censoring.mp4 6.3 MB
  • 02. Survival Analysis/8. Kaplan-Meyer Estimator.mp4 6.2 MB
  • 03. Cox Proportional Hazard Regression/6. Python - Regression Summary Visualization.mp4 6.2 MB
  • 10. XGBoost and SHAP/3. Python - Importing Libraries.mp4 6.0 MB
  • 02. Survival Analysis/12. Python - Plotting Survival Curves.mp4 5.9 MB
  • 04. CHAID/14. Python - CHAID Preparation.mp4 5.9 MB
  • 10. XGBoost and SHAP/15. Evaluate Regression-based Problems.mp4 5.7 MB
  • 10. XGBoost and SHAP/5. Introducing XGBoost.mp4 5.6 MB
  • 03. Cox Proportional Hazard Regression/7. Cox Proportional Hazard Regression Challenge.pdf 5.6 MB
  • 06. Dimension Reduction/10. Python - Cumulative Explained Variance.mp4 5.6 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/6. AIC, BIC, and Step-by-Step Guide.mp4 5.4 MB
  • 04. CHAID/9. Python - Visualizing Jobs Variable.mp4 5.3 MB
  • 07. Association Rule Learning/8. Apriori algorithm.mp4 5.3 MB
  • 10. XGBoost and SHAP/22. Python - Global Interpretability.mp4 5.1 MB
  • 03. Cox Proportional Hazard Regression/1. Game Plan.mp4 5.1 MB
  • 06. Dimension Reduction/1. Game Plan.mp4 5.0 MB
  • 10. XGBoost and SHAP/10. Python - Training and Test Set.mp4 4.9 MB
  • 07. Association Rule Learning/2. Step by Step Guide and Case Study Briefing.mp4 4.9 MB
  • 08. Random Forest and Feature Selection/5. Python - Transforming Categorical Variables.mp4 4.8 MB
  • 10. XGBoost and SHAP/11. Python - XGBoost Matrices.mp4 4.8 MB
  • 04. CHAID/2. Case Study Briefing and Step by Step Guide.mp4 4.7 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/1. Game Plan.mp4 4.7 MB
  • 06. Dimension Reduction/16. Dimension Reduction Challenge.pdf 4.2 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/8. Python - Gaussian Mixture Model.mp4 4.1 MB
  • 02. Survival Analysis/15. Python - Subsetting Dataframe.mp4 3.9 MB
  • 02. Survival Analysis/3. Case Study Briefing and Step by Step Guide.mp4 3.7 MB
  • 10. XGBoost and SHAP/2. Case Study Briefing and Step by Step Guide.mp4 3.4 MB
  • 07. Association Rule Learning/1. Game Plan.mp4 3.2 MB
  • 04. CHAID/13. Python - Modify other variables to dummy variables.mp4 3.2 MB
  • 03. Cox Proportional Hazard Regression/3. Case Study Briefing and Step by Step Guide.mp4 3.2 MB
  • 07. Association Rule Learning/11. Association Rule Learning Challenge.pdf 3.1 MB
  • 02. Survival Analysis/14. Log Rank Test.mp4 3.0 MB
  • 10. XGBoost and SHAP/1. Game Plan for XGBoost and SHAP.mp4 3.0 MB
  • 10. XGBoost and SHAP/9. Python - Isolate X and Y.mp4 2.8 MB
  • 08. Random Forest and Feature Selection/1. Game Plan for Random Forest.mp4 2.7 MB
  • 10. XGBoost and SHAP/16. Python - Predictions.mp4 2.6 MB
  • 05. Cluster Analysis - Gaussian Mixture Model/12. Gaussian Mixture Model Challenge.pdf 2.6 MB
  • 10. XGBoost and SHAP/19. Python - Preparing SHAP.mp4 2.5 MB
  • 09. LIME - Explainable Artificial Intelligence/1. Game Plan for Explainable Artificial Intelligence.mp4 2.5 MB
  • 09. LIME - Explainable Artificial Intelligence/5. LIME Challenge.pdf 2.4 MB
  • 10. XGBoost and SHAP/23. XGBoost and SHAP Challenge.pdf 2.3 MB
  • 04. CHAID/17. CHAID Challenge.pdf 2.2 MB
  • 08. Random Forest and Feature Selection/13. Random Forest Challenge.pdf 2.1 MB
  • 02. Survival Analysis/19. Survival Analysis Challenge.pdf 2.0 MB
  • 10. XGBoost and SHAP/8. XGBoost quirks.mp4 1.8 MB
  • 04. CHAID/16. Python - Data Visualization with CHAID Model.vtt 15.2 kB
  • 10. XGBoost and SHAP/24. Python - Challenge Solutions.vtt 13.7 kB
  • 06. Dimension Reduction/17. Python - Challenge Solutions.vtt 13.4 kB
  • 04. CHAID/18. Python - Challenge solutions.vtt 12.2 kB
  • 09. LIME - Explainable Artificial Intelligence/6. Python - Challenge Solutions.vtt 9.9 kB
  • 07. Association Rule Learning/12. Python - Challenge Solutions.vtt 9.5 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/13. Python - Challenge solutions.vtt 9.5 kB
  • 02. Survival Analysis/20. Python - Survival Analysis Challenge Solutions.vtt 9.1 kB
  • 04. CHAID/17. Extra Resources and Challenge.vtt 8.9 kB
  • 09. LIME - Explainable Artificial Intelligence/3. Python - Preparing LIME.vtt 8.7 kB
  • 08. Random Forest and Feature Selection/14. Python - Challenge Solutions.vtt 8.0 kB
  • 06. Dimension Reduction/12. Python - PCA interpretation.vtt 7.5 kB
  • 08. Random Forest and Feature Selection/13. Extra Resources and Challenge.vtt 7.4 kB
  • 08. Random Forest and Feature Selection/9. Confusion Matrix, AUC, and F1-Score.vtt 7.4 kB
  • 03. Cox Proportional Hazard Regression/8. Python - Solution Challenges.vtt 7.0 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/3. Gaussian Mixture Model vs. Kmeans.vtt 6.8 kB
  • 08. Random Forest and Feature Selection/6. Random Forest.vtt 6.7 kB
  • 01. Introduction/3. Course Resources, Material, and Colab setup - Important!.vtt 6.7 kB
  • 06. Dimension Reduction/16. Extra Resources and Challenge.vtt 6.5 kB
  • 04. CHAID/12. Python - Transform Minimum Variable.vtt 6.3 kB
  • 03. Cox Proportional Hazard Regression/4. Python - Preparing Script and Data.vtt 6.2 kB
  • 04. CHAID/7. CHAID Statistics and Quirks.vtt 6.1 kB
  • 07. Association Rule Learning/9. Python - Association Rule Learning.vtt 6.0 kB
  • 10. XGBoost and SHAP/20. Python - Local Interpretability.vtt 6.0 kB
  • 03. Cox Proportional Hazard Regression/7. Extra Resources and Challenge.vtt 5.8 kB
  • 07. Association Rule Learning/5. Association Rule Learning.vtt 5.8 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/12. Extra Resources and Challenge.vtt 5.8 kB
  • 10. XGBoost and SHAP/23. Extra Resources and Challenge.vtt 5.8 kB
  • 02. Survival Analysis/11. Python - Calculating Specific Events.vtt 5.7 kB
  • 04. CHAID/6. Introducing CHAID.vtt 5.7 kB
  • 04. CHAID/15. Python - CHAID Model.vtt 5.7 kB
  • 04. CHAID/10. Python - Transforming Jobs Variable.vtt 5.7 kB
  • 07. Association Rule Learning/11. Extra Resources and Challenge.vtt 5.5 kB
  • 06. Dimension Reduction/13. Manifold Learning and t-SNE.vtt 5.4 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/2. Case Study Briefing and Clustering.vtt 5.3 kB
  • 10. XGBoost and SHAP/18. SHAP.vtt 5.3 kB
  • 06. Dimension Reduction/2. What is Dimension Reduction.vtt 5.2 kB
  • 02. Survival Analysis/19. Extra Resources and Survival Analysis Challenge.vtt 5.2 kB
  • 09. LIME - Explainable Artificial Intelligence/5. Extra Resources and Challenge.vtt 5.1 kB
  • 10. XGBoost and SHAP/17. Python - MAE and RSME.vtt 4.9 kB
  • 07. Association Rule Learning/6. Python - Create Transaction List.vtt 4.7 kB
  • 08. Random Forest and Feature Selection/11. Python - Classification Report.vtt 4.5 kB
  • 07. Association Rule Learning/7. Python - Encoding Transactions.vtt 4.5 kB
  • 04. CHAID/11. Python - Transforming Experience Variable.vtt 4.5 kB
  • 10. XGBoost and SHAP/12. XGBoost Parameters.vtt 4.4 kB
  • 09. LIME - Explainable Artificial Intelligence/2. LIME.vtt 4.3 kB
  • 02. Survival Analysis/2. Survival Analyisis Introduction.vtt 4.2 kB
  • 09. LIME - Explainable Artificial Intelligence/4. Python - Explaining Predictions.vtt 4.1 kB
  • 10. XGBoost and SHAP/7. How XGBoost works part 2.vtt 4.1 kB
  • 06. Dimension Reduction/7. Python - Correlation Matrix.vtt 4.1 kB
  • 06. Dimension Reduction/3. Principal Component Analysis.vtt 4.0 kB
  • 02. Survival Analysis/4. Python - Changing Directory.vtt 4.0 kB
  • 08. Random Forest and Feature Selection/7. Python - Training and Test Set.vtt 4.0 kB
  • 10. XGBoost and SHAP/13. Python - XGBoost Parameters.vtt 3.9 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/9. Python - Cluster Prediction.vtt 3.9 kB
  • 06. Dimension Reduction/9. Python - Optimal Number of Components.vtt 3.9 kB
  • 04. CHAID/4. Python - Installing libraries.vtt 3.8 kB
  • 03. Cox Proportional Hazard Regression/2. Cox Proportional Hazard Regression.vtt 3.8 kB
  • 04. CHAID/3. Problem Statement.vtt 3.8 kB
  • 10. XGBoost and SHAP/15. Evaluate Regression-based Problems.vtt 3.8 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/10. Python - Probability of belonging to each cluster.vtt 3.7 kB
  • 10. XGBoost and SHAP/6. How XGBoost works part 1.vtt 3.6 kB
  • 10. XGBoost and SHAP/5. Introducing XGBoost.vtt 3.6 kB
  • 02. Survival Analysis/9. Censoring.vtt 3.6 kB
  • 06. Dimension Reduction/15. Python -Visualizing Manifold Learning.vtt 3.6 kB
  • 10. XGBoost and SHAP/21. Python - Dependency Plots.vtt 3.5 kB
  • 04. CHAID/8. Python - Removing column and unique values check.vtt 3.4 kB
  • 06. Dimension Reduction/4. Python - Importing Libraries.vtt 3.3 kB
  • 02. Survival Analysis/6. Python - Loading Data.vtt 3.3 kB
  • 02. Survival Analysis/8. Kaplan-Meyer Estimator.vtt 3.3 kB
  • 04. CHAID/1. Game Plan.vtt 3.3 kB
  • 08. Random Forest and Feature Selection/2. Case Study Briefing and Step by Step Guide.vtt 3.1 kB
  • 02. Survival Analysis/10. Python - Kaplan-Meyer Estimator.vtt 3.1 kB
  • 10. XGBoost and SHAP/14. Python - XGBoost Model.vtt 3.1 kB
  • 11. Bonus Section/1. Bonus Lecture.html 3.1 kB
  • 04. CHAID/5. Python - Importing Libraries and Data.vtt 3.0 kB
  • 01. Introduction/1. Introduction to Data Mining course for Business Analytics & Data Analysis.vtt 3.0 kB
  • 02. Survival Analysis/7. Python - Transforming Dependent Variable.vtt 2.9 kB
  • 02. Survival Analysis/16. Python - Kaplan-Meyer Estimator per Gender.vtt 2.9 kB
  • 08. Random Forest and Feature Selection/12. Python .- Feature Importance for Business Analytics.vtt 2.9 kB
  • 06. Dimension Reduction/11. Python - PCA.vtt 2.8 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/6. AIC, BIC, and Step-by-Step Guide.vtt 2.8 kB
  • 01. Introduction/4. Diogo's Introduction and Background.vtt 2.8 kB
  • 08. Random Forest and Feature Selection/3. Python - Importing Libraries.vtt 2.8 kB
  • 07. Association Rule Learning/8. Apriori algorithm.vtt 2.8 kB
  • 10. XGBoost and SHAP/2. Case Study Briefing and Step by Step Guide.vtt 2.8 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/4. Python - Changing Directory and Importing Libraries.vtt 2.7 kB
  • 02. Survival Analysis/1. Game Plan for Survival Analysis section.vtt 2.7 kB
  • 04. CHAID/14. Python - CHAID Preparation.vtt 2.7 kB
  • 10. XGBoost and SHAP/4. Python - Loading Data.vtt 2.7 kB
  • 08. Random Forest and Feature Selection/10. Python - Random Forest Predictions.vtt 2.7 kB
  • 06. Dimension Reduction/14. Python - t-SNE.vtt 2.6 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/5. Python - Loading Data.vtt 2.6 kB
  • 10. XGBoost and SHAP/22. Python - Global Interpretability.vtt 2.6 kB
  • 02. Survival Analysis/3. Case Study Briefing and Step by Step Guide.vtt 2.6 kB
  • 02. Survival Analysis/13. Python - Plotting Cumulative Curves.vtt 2.6 kB
  • 03. Cox Proportional Hazard Regression/3. Case Study Briefing and Step by Step Guide.vtt 2.5 kB
  • 02. Survival Analysis/17. Python - Plotting both Survival Curves.vtt 2.5 kB
  • 03. Cox Proportional Hazard Regression/6. Python - Regression Summary Visualization.vtt 2.4 kB
  • 02. Survival Analysis/12. Python - Plotting Survival Curves.vtt 2.4 kB
  • 06. Dimension Reduction/1. Game Plan.vtt 2.3 kB
  • 10. XGBoost and SHAP/10. Python - Training and Test Set.vtt 2.3 kB
  • 07. Association Rule Learning/3. Python - Importing Libraries.vtt 2.1 kB
  • 02. Survival Analysis/5. Python - Importing Libraries.vtt 2.1 kB
  • 06. Dimension Reduction/10. Python - Cumulative Explained Variance.vtt 2.0 kB
  • 04. CHAID/9. Python - Visualizing Jobs Variable.vtt 2.0 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/1. Game Plan.vtt 1.9 kB
  • 08. Random Forest and Feature Selection/4. Python - Loading Data.vtt 1.9 kB
  • 06. Dimension Reduction/6. Python - Transforming String Variables.vtt 1.9 kB
  • 08. Random Forest and Feature Selection/8. Python - Random Forest.vtt 1.9 kB
  • 03. Cox Proportional Hazard Regression/1. Game Plan.vtt 1.9 kB
  • 10. XGBoost and SHAP/3. Python - Importing Libraries.vtt 1.8 kB
  • 02. Survival Analysis/15. Python - Subsetting Dataframe.vtt 1.8 kB
  • 07. Association Rule Learning/4. Python - Loading Data.vtt 1.8 kB
  • 02. Survival Analysis/14. Log Rank Test.vtt 1.7 kB
  • 10. XGBoost and SHAP/11. Python - XGBoost Matrices.vtt 1.7 kB
  • 08. Random Forest and Feature Selection/5. Python - Transforming Categorical Variables.vtt 1.6 kB
  • 10. XGBoost and SHAP/1. Game Plan for XGBoost and SHAP.vtt 1.6 kB
  • 07. Association Rule Learning/1. Game Plan.vtt 1.6 kB
  • 10. XGBoost and SHAP/16. Python - Predictions.vtt 1.6 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/8. Python - Gaussian Mixture Model.vtt 1.5 kB
  • 10. XGBoost and SHAP/9. Python - Isolate X and Y.vtt 1.4 kB
  • 08. Random Forest and Feature Selection/1. Game Plan for Random Forest.vtt 1.3 kB
  • 10. XGBoost and SHAP/19. Python - Preparing SHAP.vtt 1.3 kB
  • 10. XGBoost and SHAP/8. XGBoost quirks.vtt 1.3 kB
  • 04. CHAID/13. Python - Modify other variables to dummy variables.vtt 1.2 kB
  • 09. LIME - Explainable Artificial Intelligence/1. Game Plan for Explainable Artificial Intelligence.vtt 1.1 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/14. Will you help me.vtt 1.0 kB
  • 05. Cluster Analysis - Gaussian Mixture Model/15. Your feedback is invaluable.html 521 Bytes
  • 10. XGBoost and SHAP/25. End of Course Feedback.html 447 Bytes
  • 01. Introduction/2. Your resources.html 371 Bytes

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