[Tutorialsplanet.NET] Udemy - Machine Learning 101 with Scikit-learn and StatsModels

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[Tutorialsplanet.NET] Udemy - Machine Learning 101 with Scikit-learn and StatsModels

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收录时间：2021-03-28
最近下载：2024-11-29

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文件列表

  • 5. Linear Regression - Practical Example/1. Practical Example (Part 1).mp4 101.8 MB
  • 6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.mp4 79.5 MB
  • 7. Cluster Analysis/17. What Can be Done with Cluster Analysis.mp4 66.6 MB
  • 7. Cluster Analysis/2. Examples of Clustering.mp4 66.0 MB
  • 6. Logistic Regression/14. Underfitting and Overfitting.srt 62.2 MB
  • 5. Linear Regression - Practical Example/8. Practical Example (Part 5).mp4 60.7 MB
  • 5. Linear Regression - Practical Example/6. Practical Example (Part 4).mp4 58.7 MB
  • 1. Introduction/1. What Does the Course Cover.mp4 54.0 MB
  • 3. Linear Regression with StatsModels/38. Dealing with Categorical Data.mp4 53.9 MB
  • 3. Linear Regression with StatsModels/3. The Linear Regression Model.mp4 53.5 MB
  • 7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).mp4 53.3 MB
  • 3. Linear Regression with StatsModels/23. Adjusted R-Squared.mp4 52.1 MB
  • 4. Linear Regression with Sklearn/19. Training and Testing.mp4 51.6 MB
  • 7. Cluster Analysis/6. A Hands on Example of K-Means.mp4 51.0 MB
  • 5. Linear Regression - Practical Example/2. Practical Example (Part 2).mp4 48.2 MB
  • 7. Cluster Analysis/1. Introduction to Cluster Analysis.mp4 46.8 MB
  • 3. Linear Regression with StatsModels/15. SST, SSR, and SSE.mp4 44.3 MB
  • 3. Linear Regression with StatsModels/10. Simple Linear Regression in Python.mp4 43.8 MB
  • 2. Setting Up The Working Environment/2. Why Python and Why Jupyter.mp4 43.1 MB
  • 3. Linear Regression with StatsModels/13. What Does the StatsModels Summary Regression Table Tell us.mp4 42.9 MB
  • 7. Cluster Analysis/10. The Elbow Method or How to Choose the Number of Clusters.mp4 42.3 MB
  • 7. Cluster Analysis/15. Practical Example Market Segmentation (Part 1).srt 42.2 MB
  • 7. Cluster Analysis/15. Practical Example Market Segmentation (Part 1).mp4 42.2 MB
  • 3. Linear Regression with StatsModels/33. A3 Normality and Homoscedasticity.mp4 41.9 MB
  • 3. Linear Regression with StatsModels/38. Dealing with Categorical Data.srt 41.5 MB
  • 4. Linear Regression with Sklearn/14. Feature Scaling.mp4 41.0 MB
  • 8. Cluster Analysis Additional Topics/1. Other Types of Clustering.mp4 38.9 MB
  • 3. Linear Regression with StatsModels/19. Goodness of Fit The R-Squared.mp4 38.8 MB
  • 3. Linear Regression with StatsModels/9. Python Packages Installation.mp4 38.6 MB
  • 6. Logistic Regression/10. Dummies in a Logistic Regression.mp4 37.3 MB
  • 2. Setting Up The Working Environment/4. Installing Anaconda.mp4 36.6 MB
  • 4. Linear Regression with Sklearn/15. Feature Selection through Standardization.mp4 36.6 MB
  • 4. Linear Regression with Sklearn/3. Simple Linear Regression with sklearn.mp4 36.5 MB
  • 7. Cluster Analysis/12. Pros and Cons of K-Means.mp4 34.2 MB
  • 3. Linear Regression with StatsModels/31. A2 No Endogeneity.mp4 34.0 MB
  • 6. Logistic Regression/2. A Simple Example of a Logistic Regression in Python.mp4 33.7 MB
  • 4. Linear Regression with Sklearn/4. Simple Linear Regression with sklearn - Summary Table.mp4 33.6 MB
  • 4. Linear Regression with Sklearn/8. Adjusted R-Squared.mp4 32.4 MB
  • 6. Logistic Regression/15. Testing our Model and Bulding a Confusion Matrix.mp4 32.0 MB
  • 7. Cluster Analysis/3. Classification vs Clustering.mp4 31.9 MB
  • 4. Linear Regression with Sklearn/10. Feature Selection through p-values (F-regression).mp4 31.0 MB
  • 6. Logistic Regression/12. Assessing the Accuracy of a Classification Model.mp4 31.0 MB
  • 6. Logistic Regression/9. Interpreting the Odds Ratio.mp4 30.3 MB
  • 3. Linear Regression with StatsModels/34. A4 No Autocorrelation.mp4 30.0 MB
  • 6. Logistic Regression/7. Going through the Regression Summary Table.mp4 29.2 MB
  • 8. Cluster Analysis Additional Topics/3. Heatmaps.mp4 28.7 MB
  • 4. Linear Regression with Sklearn/1. What is sklearn.mp4 28.6 MB
  • 7. Cluster Analysis/13. Standardization of Features when Clustering.mp4 28.1 MB
  • 8. Cluster Analysis Additional Topics/2. The Dendrogram.mp4 28.1 MB
  • 3. Linear Regression with StatsModels/36. A5 No Multicollinearity.mp4 27.9 MB
  • 3. Linear Regression with StatsModels/17. The Ordinary Least Squares (OLS).mp4 27.3 MB
  • 4. Linear Regression with Sklearn/16. Making Predictions with Standardized Coefficients.mp4 27.2 MB
  • 7. Cluster Analysis/5. K-Means Clustering.mp4 26.2 MB
  • 5. Linear Regression - Practical Example/4. Practical Example (Part 3).mp4 24.9 MB
  • 6. Logistic Regression/1. Introduction to Logistic Regression.mp4 24.6 MB
  • 3. Linear Regression with StatsModels/40. Making Predictions.mp4 24.2 MB
  • 2. Setting Up The Working Environment/6. The Jupyter Dashboard - Part 2.mp4 22.3 MB
  • 6. Logistic Regression/6. A Coding Tip (optional).mp4 22.1 MB
  • 6. Logistic Regression/14. Underfitting and Overfitting.mp4 21.4 MB
  • 7. Cluster Analysis/8. Categorical Data in Cluster Analysis.mp4 21.2 MB
  • 4. Linear Regression with Sklearn/7. Multiple Linear Regression with sklearn.mp4 21.0 MB
  • 3. Linear Regression with StatsModels/27. Assumptions of the OLS Framework.mp4 20.3 MB
  • 4. Linear Regression with Sklearn/2. Game Plan for sklearn.mp4 20.3 MB
  • 3. Linear Regression with StatsModels/21. The Multiple Linear Regression Model.mp4 20.0 MB
  • 4. Linear Regression with Sklearn/18. Underfitting and Overfitting.mp4 17.8 MB
  • 6. Logistic Regression/4. Your First Logistic Regression.mp4 16.7 MB
  • 3. Linear Regression with StatsModels/26. F-Statistic and F-Test for a Linear Regression.mp4 15.8 MB
  • 3. Linear Regression with StatsModels/1. Introduction to Regression Analysis.mp4 15.4 MB
  • 7. Cluster Analysis/4. Math Concepts Needed to Proceed.mp4 14.1 MB
  • 3. Linear Regression with StatsModels/5. Correlation vs Regression.mp4 13.3 MB
  • 4. Linear Regression with Sklearn/12. Creating a Summary Table with the p-values.mp4 12.9 MB
  • 3. Linear Regression with StatsModels/29. A1 Linearity.mp4 12.6 MB
  • 3. Linear Regression with StatsModels/12. What is Seaborn.mp4 11.5 MB
  • 2. Setting Up The Working Environment/5. The Jupyter Dashboard - Part 1.mp4 10.0 MB
  • 7. Cluster Analysis/14. Cluster Analysis and Regression Analysis.mp4 9.6 MB
  • 2. Setting Up The Working Environment/9. Installing sklearn.mp4 8.2 MB
  • 2. Setting Up The Working Environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp4 5.5 MB
  • 3. Linear Regression with StatsModels/7. Geometrical Representation.mp4 5.2 MB
  • 2. Setting Up The Working Environment/7.1 Shortcuts-for-Jupyter.pdf 634.0 kB
  • 6. Logistic Regression/1.1 Course_Notes_Logistic_Regression.pdf 343.2 kB
  • 6. Logistic Regression/2.2 Course_Notes_Logistic_Regression.pdf 343.2 kB
  • 3. Linear Regression with StatsModels/1.1 Course notes_regression_analysis.pdf 319.7 kB
  • 3. Linear Regression with StatsModels/3.1 Course notes_regression_analysis.pdf 319.7 kB
  • 7. Cluster Analysis/1.1 Course_Notes_Cluster_Analysis.pdf 213.7 kB
  • 7. Cluster Analysis/2.1 Course_Notes_Cluster_Analysis.pdf 213.7 kB
  • 5. Linear Regression - Practical Example/1. Practical Example (Part 1).srt 15.2 kB
  • 5. Linear Regression - Practical Example/6. Practical Example (Part 4).srt 11.8 kB
  • 5. Linear Regression - Practical Example/8. Practical Example (Part 5).srt 10.8 kB
  • 4. Linear Regression with Sklearn/19. Training and Testing.srt 9.8 kB
  • 7. Cluster Analysis/6. A Hands on Example of K-Means.srt 9.4 kB
  • 7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).srt 9.1 kB
  • 5. Linear Regression - Practical Example/2. Practical Example (Part 2).srt 8.2 kB
  • 3. Linear Regression with StatsModels/10. Simple Linear Regression in Python.srt 8.1 kB
  • 4. Linear Regression with Sklearn/14. Feature Scaling.srt 7.9 kB
  • 8. Cluster Analysis Additional Topics/2. The Dendrogram.srt 7.6 kB
  • 4. Linear Regression with Sklearn/3. Simple Linear Regression with sklearn.srt 7.5 kB
  • 4. Linear Regression with Sklearn/15. Feature Selection through Standardization.srt 7.4 kB
  • 7. Cluster Analysis/10. The Elbow Method or How to Choose the Number of Clusters.srt 7.4 kB
  • 3. Linear Regression with StatsModels/23. Adjusted R-Squared.srt 7.3 kB
  • 3. Linear Regression with StatsModels/3. The Linear Regression Model.srt 7.1 kB
  • 2. Setting Up The Working Environment/6. The Jupyter Dashboard - Part 2.srt 6.9 kB
  • 4. Linear Regression with Sklearn/4. Simple Linear Regression with sklearn - Summary Table.srt 6.9 kB
  • 4. Linear Regression with Sklearn/10. Feature Selection through p-values (F-regression).srt 6.8 kB
  • 3. Linear Regression with StatsModels/19. Goodness of Fit The R-Squared.srt 6.8 kB
  • 3. Linear Regression with StatsModels/33. A3 Normality and Homoscedasticity.srt 6.7 kB
  • 7. Cluster Analysis/5. K-Means Clustering.srt 6.7 kB
  • 3. Linear Regression with StatsModels/13. What Does the StatsModels Summary Regression Table Tell us.srt 6.7 kB
  • 6. Logistic Regression/15. Testing our Model and Bulding a Confusion Matrix.srt 6.6 kB
  • 8. Cluster Analysis Additional Topics/3. Heatmaps.srt 6.5 kB
  • 7. Cluster Analysis/17. What Can be Done with Cluster Analysis.srt 6.5 kB
  • 2. Setting Up The Working Environment/2. Why Python and Why Jupyter.srt 6.5 kB
  • 4. Linear Regression with Sklearn/8. Adjusted R-Squared.srt 6.4 kB
  • 7. Cluster Analysis/2. Examples of Clustering.srt 6.3 kB
  • 1. Introduction/1. What Does the Course Cover.srt 6.2 kB
  • 7. Cluster Analysis/13. Standardization of Features when Clustering.srt 6.1 kB
  • 6. Logistic Regression/2. A Simple Example of a Logistic Regression in Python.srt 5.9 kB
  • 4. Linear Regression with Sklearn/16. Making Predictions with Standardized Coefficients.srt 5.7 kB
  • 6. Logistic Regression/10. Dummies in a Logistic Regression.srt 5.5 kB
  • 3. Linear Regression with StatsModels/9. Python Packages Installation.srt 5.5 kB
  • 3. Linear Regression with StatsModels/31. A2 No Endogeneity.srt 5.4 kB
  • 6. Logistic Regression/7. Going through the Regression Summary Table.srt 5.2 kB
  • 6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.srt 5.0 kB
  • 3. Linear Regression with StatsModels/34. A4 No Autocorrelation.srt 4.9 kB
  • 7. Cluster Analysis/1. Introduction to Cluster Analysis.srt 4.9 kB
  • 3. Linear Regression with StatsModels/36. A5 No Multicollinearity.srt 4.8 kB
  • 6. Logistic Regression/9. Interpreting the Odds Ratio.srt 4.8 kB
  • 8. Cluster Analysis Additional Topics/1. Other Types of Clustering.srt 4.8 kB
  • 2. Setting Up The Working Environment/4. Installing Anaconda.srt 4.7 kB
  • 7. Cluster Analysis/12. Pros and Cons of K-Means.srt 4.7 kB
  • 3. Linear Regression with StatsModels/40. Making Predictions.srt 4.6 kB
  • 6. Logistic Regression/12. Assessing the Accuracy of a Classification Model.srt 4.5 kB
  • 4. Linear Regression with Sklearn/7. Multiple Linear Regression with sklearn.srt 4.3 kB
  • 5. Linear Regression - Practical Example/4. Practical Example (Part 3).srt 4.2 kB
  • 7. Cluster Analysis/4. Math Concepts Needed to Proceed.srt 4.2 kB
  • 3. Linear Regression with StatsModels/15. SST, SSR, and SSE.srt 4.1 kB
  • 3. Linear Regression with StatsModels/17. The Ordinary Least Squares (OLS).srt 3.6 kB
  • 4. Linear Regression with Sklearn/18. Underfitting and Overfitting.srt 3.5 kB
  • 6. Logistic Regression/4. Your First Logistic Regression.srt 3.5 kB
  • 4. Linear Regression with Sklearn/1. What is sklearn.srt 3.5 kB
  • 7. Cluster Analysis/3. Classification vs Clustering.srt 3.3 kB
  • 7. Cluster Analysis/8. Categorical Data in Cluster Analysis.srt 3.3 kB
  • 3. Linear Regression with StatsModels/21. The Multiple Linear Regression Model.srt 3.3 kB
  • 2. Setting Up The Working Environment/5. The Jupyter Dashboard - Part 1.srt 3.2 kB
  • 6. Logistic Regression/6. A Coding Tip (optional).srt 3.2 kB
  • 4. Linear Regression with Sklearn/12. Creating a Summary Table with the p-values.srt 3.1 kB
  • 3. Linear Regression with StatsModels/27. Assumptions of the OLS Framework.srt 3.0 kB
  • 4. Linear Regression with Sklearn/2. Game Plan for sklearn.srt 3.0 kB
  • 3. Linear Regression with StatsModels/26. F-Statistic and F-Test for a Linear Regression.srt 2.6 kB
  • 3. Linear Regression with StatsModels/29. A1 Linearity.srt 2.5 kB
  • 7. Cluster Analysis/14. Cluster Analysis and Regression Analysis.srt 2.2 kB
  • 3. Linear Regression with StatsModels/1. Introduction to Regression Analysis.srt 2.2 kB
  • 3. Linear Regression with StatsModels/5. Correlation vs Regression.srt 1.9 kB
  • 8. Cluster Analysis Additional Topics/4. Completing 100%.html 1.9 kB
  • 2. Setting Up The Working Environment/9. Installing sklearn.srt 1.7 kB
  • 3. Linear Regression with StatsModels/7. Geometrical Representation.srt 1.7 kB
  • 6. Logistic Regression/1. Introduction to Logistic Regression.srt 1.6 kB
  • 3. Linear Regression with StatsModels/12. What is Seaborn.srt 1.6 kB
  • 2. Setting Up The Working Environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.srt 1.4 kB
  • 3. Linear Regression with StatsModels/11. Simple Linear Regression in Python - Exercise.html 1.4 kB
  • 5. Linear Regression - Practical Example/3. A Note on Multicollinearity.html 849 Bytes
  • 4. Linear Regression with Sklearn/5. A Note on Normalization.html 733 Bytes
  • 5. Linear Regression - Practical Example/7. Dummy Variables Interpretation - Exercise.html 713 Bytes
  • 2. Setting Up The Working Environment/11. Installing Packages - Solution.html 668 Bytes
  • 5. Linear Regression - Practical Example/9. Linear Regression - Exercise.html 503 Bytes
  • 4. Linear Regression with Sklearn/11. A Note on Calculation of P-values with sklearn.html 433 Bytes
  • 2. Setting Up The Working Environment/10. Installing Packages - Exercise.html 409 Bytes
  • 2. Setting Up The Working Environment/7. Jupyter Shortcuts.html 305 Bytes
  • 2. Setting Up The Working Environment/3. Why Python and Why Jupyter.html 166 Bytes
  • 2. Setting Up The Working Environment/8. The Jupyter Dashboard.html 166 Bytes
  • 3. Linear Regression with StatsModels/14. What Does the StatsModels Summary Regression Table Tell us.html 166 Bytes
  • 3. Linear Regression with StatsModels/16. SST, SSR, and SSE.html 166 Bytes
  • 3. Linear Regression with StatsModels/18. The Ordinary Least Squares (OLS).html 166 Bytes
  • 3. Linear Regression with StatsModels/2. Introduction to Regression Analysis.html 166 Bytes
  • 3. Linear Regression with StatsModels/20. Goodness of Fit The R-Squared.html 166 Bytes
  • 3. Linear Regression with StatsModels/22. Multiple Linear Regression.html 166 Bytes
  • 3. Linear Regression with StatsModels/24. Adjusted R-Squared.html 166 Bytes
  • 3. Linear Regression with StatsModels/28. Assumptions of the OLS Framework.html 166 Bytes
  • 3. Linear Regression with StatsModels/30. A1 Linearity.html 166 Bytes
  • 3. Linear Regression with StatsModels/32. A2 No Endogeneity.html 166 Bytes
  • 3. Linear Regression with StatsModels/35. A4 No Autocorrelation.html 166 Bytes
  • 3. Linear Regression with StatsModels/37. A5 No Multicollinearity.html 166 Bytes
  • 3. Linear Regression with StatsModels/4. The Linear Regression Model.html 166 Bytes
  • 3. Linear Regression with StatsModels/6. Correlation vs Regression.html 166 Bytes
  • 3. Linear Regression with StatsModels/8. Geometrical Representation.html 166 Bytes
  • 3. Linear Regression with StatsModels/10.1 Simple Linear Regression in Python - Lecture.html 134 Bytes
  • 3. Linear Regression with StatsModels/11.1 Simple Linear Regression in Python - Exercise.html 134 Bytes
  • 3. Linear Regression with StatsModels/23.1 Multiple Linear Regression and Adjusted R-squared - Lecture.html 134 Bytes
  • 3. Linear Regression with StatsModels/25.1 Multiple Linear Regression - Exercise.html 134 Bytes
  • 3. Linear Regression with StatsModels/38.1 Dealing with Categorical Data - Lecture.html 134 Bytes
  • 3. Linear Regression with StatsModels/39.1 Dealing with Categorical Data - Exercise.html 134 Bytes
  • 3. Linear Regression with StatsModels/40.1 Making Predictions - Lecture.html 134 Bytes
  • 4. Linear Regression with Sklearn/10.1 Feature Selection through p-values (F-regression).html 134 Bytes
  • 4. Linear Regression with Sklearn/12.1 Creating a Summary Table with the p-values.html 134 Bytes
  • 4. Linear Regression with Sklearn/13.1 Multiple Linear Regression - Exercise.html 134 Bytes
  • 4. Linear Regression with Sklearn/14.1 Feature Scaling.html 134 Bytes
  • 4. Linear Regression with Sklearn/15.1 Feature Selection through Standardization.html 134 Bytes
  • 4. Linear Regression with Sklearn/16.1 Making Predictions with Standardized Coefficients.html 134 Bytes
  • 4. Linear Regression with Sklearn/17.1 Feature Scaling - Exercise.html 134 Bytes
  • 4. Linear Regression with Sklearn/19.1 Training and Testing.html 134 Bytes
  • 4. Linear Regression with Sklearn/3.1 Simple Linear Regression with sklearn.html 134 Bytes
  • 4. Linear Regression with Sklearn/4.1 Simple Linear Regression with sklearn.html 134 Bytes
  • 4. Linear Regression with Sklearn/6.1 Simple Linear Regression with sklearn - Exercise.html 134 Bytes
  • 4. Linear Regression with Sklearn/7.1 Multiple Linear Regression with sklearn.html 134 Bytes
  • 4. Linear Regression with Sklearn/8.1 Multiple Linear Regression and Adjusted R-squared - Lecture.html 134 Bytes
  • 4. Linear Regression with Sklearn/9.1 Multiple Linear Regression and Adjusted R-squared - Exercise.html 134 Bytes
  • 5. Linear Regression - Practical Example/1.1 Practical Example (Part 1).html 134 Bytes
  • 5. Linear Regression - Practical Example/2.1 Practical Example (Part 2).html 134 Bytes
  • 5. Linear Regression - Practical Example/4.1 Practical Example (Part 3).html 134 Bytes
  • 5. Linear Regression - Practical Example/5.1 Dummies and VIF - Exercise and Solution.html 134 Bytes
  • 5. Linear Regression - Practical Example/6.1 Practical Example (Part 4).html 134 Bytes
  • 5. Linear Regression - Practical Example/8.1 Practical Example (Part 5).html 134 Bytes
  • 6. Logistic Regression/10.1 Dummies in a Logistic Regression - Lecture.html 134 Bytes
  • 6. Logistic Regression/11.1 Dummies in a Logistic Regression - Exercise.html 134 Bytes
  • 6. Logistic Regression/12.1 Assessing the Accuracy of a Classification Model.html 134 Bytes
  • 6. Logistic Regression/13.1 Assessing the Accuracy of a Classification Model - Exercise.html 134 Bytes
  • 6. Logistic Regression/15.1 Testing our Model and Bulding a Confusion Matrix.html 134 Bytes
  • 6. Logistic Regression/16.1 Testing our Model and Bulding a Confusion Matrix - Exercise.html 134 Bytes
  • 6. Logistic Regression/2.1 A Simple Example of a Logistic Regression in Python.html 134 Bytes
  • 6. Logistic Regression/4.1 Your First Logistic Regression - Lecture.html 134 Bytes
  • 6. Logistic Regression/5.1 Your First Logistic Regression - Exercise.html 134 Bytes
  • 6. Logistic Regression/8.1 Going through the Regression Summary Table - Exercise.html 134 Bytes
  • 7. Cluster Analysis/10.1 The Elbow Method or How to Choose the Number of Clusters - Lecture.html 134 Bytes
  • 7. Cluster Analysis/11.1 The Elbow Method or How to Choose the Number of Clusters - Exercise.html 134 Bytes
  • 7. Cluster Analysis/15.1 Market Segmentation (Part 1) - Lecture.html 134 Bytes
  • 7. Cluster Analysis/16.1 Market Segmentation (Part 2) - Lecture.html 134 Bytes
  • 7. Cluster Analysis/18.1 EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 134 Bytes
  • 7. Cluster Analysis/19.1 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 134 Bytes
  • 7. Cluster Analysis/6.1 A Hands on Example of K-Means - Lecture.html 134 Bytes
  • 7. Cluster Analysis/7.1 A Hands on Example of K-Means - Exercise.html 134 Bytes
  • 7. Cluster Analysis/8.1 Categorical Data in Cluster Analysis - Lecture.html 134 Bytes
  • 7. Cluster Analysis/9.1 Categorical Data in Cluster Analysis - Exercise.html 134 Bytes
  • 8. Cluster Analysis Additional Topics/3.1 Heatmaps - Lecture.html 134 Bytes
  • [Tutorialsplanet.NET].url 128 Bytes
  • 7. Cluster Analysis/18. EXERCISE Species Segmentation with Cluster Analysis (Part 1).html 87 Bytes
  • 3. Linear Regression with StatsModels/25. Multiple Linear Regression - Exercise.html 76 Bytes
  • 3. Linear Regression with StatsModels/39. Dealing with Categorical Data - Exercise.html 76 Bytes
  • 4. Linear Regression with Sklearn/13. Multiple Linear Regression - Exercise.html 76 Bytes
  • 4. Linear Regression with Sklearn/17. Feature Scaling - Exercise.html 76 Bytes
  • 4. Linear Regression with Sklearn/6. Simple Linear Regression with sklearn - Exercise.html 76 Bytes
  • 4. Linear Regression with Sklearn/9. Adjusted R-Squared - Exercise.html 76 Bytes
  • 5. Linear Regression - Practical Example/5. Dummies and VIF - Exercise.html 76 Bytes
  • 6. Logistic Regression/11. Dummies in a Logistic Regression - Exercise.html 76 Bytes
  • 6. Logistic Regression/5. Your First Logistic Regression - Exercise.html 76 Bytes
  • 6. Logistic Regression/8. Going through the Regression Summary Table - Exercise.html 76 Bytes
  • 7. Cluster Analysis/11. The Elbow Method or How to Choose the Number of Clusters - Exercise.html 60 Bytes
  • 6. Logistic Regression/13. Assessing the Accuracy of a Classification Model - Exercise.html 55 Bytes
  • 6. Logistic Regression/16. Testing our Model and Bulding a Confusion Matrix - Exercise.html 55 Bytes
  • 7. Cluster Analysis/19. EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 55 Bytes
  • 7. Cluster Analysis/7. A Hands on Example of K-Means - Exercise.html 55 Bytes
  • 7. Cluster Analysis/9. Categorical Data in Cluster Analysis - Exercise.html 55 Bytes

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