udemy-machine-learning-101-with-scikit-learn-and-stats-models

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文档列表

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
3. Linear Regression with StatsModels/1.1 Course notes_regression_analysis_jp2.zip 3.9 MB
3. Linear Regression with StatsModels/3.1 Course notes_regression_analysis_jp2.zip 3.9 MB
6. Logistic Regression/1.1 Course_Notes_Logistic_Regression_jp2.zip 2.7 MB
6. Logistic Regression/2.2 Course_Notes_Logistic_Regression_jp2.zip 2.7 MB
2. Setting Up The Working Environment/7.1 Shortcuts-for-Jupyter_jp2.zip 2.4 MB
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7. Cluster Analysis/10. The Elbow Method or How to Choose the Number of Clusters.srt 7.4 kB
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7. Cluster Analysis/5. K-Means Clustering.srt 6.7 kB
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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
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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
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3. Linear Regression with StatsModels/22. Multiple Linear Regression.html 166 Bytes
3. Linear Regression with StatsModels/24. Adjusted R-Squared.html 166 Bytes
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3. Linear Regression with StatsModels/30. A1 Linearity.html 166 Bytes
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3. Linear Regression with StatsModels/35. A4 No Autocorrelation.html 166 Bytes
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3. Linear Regression with StatsModels/6. Correlation vs Regression.html 166 Bytes
3. Linear Regression with StatsModels/8. Geometrical Representation.html 166 Bytes
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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
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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
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5. Linear Regression - Practical Example/2.1 Practical Example (Part 2).html 134 Bytes
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6. Logistic Regression/13.1 Assessing the Accuracy of a Classification Model - Exercise.html 134 Bytes
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7. Cluster Analysis/16.1 Market Segmentation (Part 2) - Lecture.html 134 Bytes
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7. Cluster Analysis/19.1 EXERCISE Species Segmentation with Cluster Analysis (Part 2).html 134 Bytes
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3. Linear Regression with StatsModels/25. Multiple Linear Regression - Exercise.html 76 Bytes
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5. Linear Regression - Practical Example/5. Dummies and VIF - Exercise.html 76 Bytes
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6. Logistic Regression/8. Going through the Regression Summary Table - Exercise.html 76 Bytes
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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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