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种子名称:
[Tutorialsplanet.NET] Udemy - Machine Learning 101 with Scikit-learn and StatsModels
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文件数目:
75个文件
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2.35 GB
收录时间:
2021-4-18 19:10
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3次
资源热度:
175
最近下载:
2024-12-14 19:44
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[Tutorialsplanet.NET] Udemy - Machine Learning 101 with Scikit-learn and StatsModels.torrent
1. Introduction/1. What Does the Course Cover.mp451.51MB
2. Setting Up The Working Environment/1. Setting Up the Environment - An Introduction (Do Not Skip, Please)!.mp45.27MB
2. Setting Up The Working Environment/2. Why Python and Why Jupyter.mp441.08MB
2. Setting Up The Working Environment/4. Installing Anaconda.mp434.95MB
2. Setting Up The Working Environment/5. The Jupyter Dashboard - Part 1.mp49.56MB
2. Setting Up The Working Environment/6. The Jupyter Dashboard - Part 2.mp421.23MB
2. Setting Up The Working Environment/9. Installing sklearn.mp47.85MB
3. Linear Regression with StatsModels/1. Introduction to Regression Analysis.mp414.68MB
3. Linear Regression with StatsModels/10. Simple Linear Regression in Python.mp441.81MB
3. Linear Regression with StatsModels/12. What is Seaborn.mp410.97MB
3. Linear Regression with StatsModels/13. What Does the StatsModels Summary Regression Table Tell us.mp440.96MB
3. Linear Regression with StatsModels/15. SST, SSR, and SSE.mp442.26MB
3. Linear Regression with StatsModels/17. The Ordinary Least Squares (OLS).mp426.01MB
3. Linear Regression with StatsModels/19. Goodness of Fit The R-Squared.mp436.96MB
3. Linear Regression with StatsModels/21. The Multiple Linear Regression Model.mp419.1MB
3. Linear Regression with StatsModels/23. Adjusted R-Squared.mp449.68MB
3. Linear Regression with StatsModels/26. F-Statistic and F-Test for a Linear Regression.mp415.06MB
3. Linear Regression with StatsModels/27. Assumptions of the OLS Framework.mp419.39MB
3. Linear Regression with StatsModels/29. A1 Linearity.mp412.05MB
3. Linear Regression with StatsModels/3. The Linear Regression Model.mp451.01MB
3. Linear Regression with StatsModels/31. A2 No Endogeneity.mp432.46MB
3. Linear Regression with StatsModels/33. A3 Normality and Homoscedasticity.mp439.99MB
3. Linear Regression with StatsModels/34. A4 No Autocorrelation.mp428.62MB
3. Linear Regression with StatsModels/36. A5 No Multicollinearity.mp426.58MB
3. Linear Regression with StatsModels/38. Dealing with Categorical Data.mp451.38MB
3. Linear Regression with StatsModels/40. Making Predictions.mp423.06MB
3. Linear Regression with StatsModels/5. Correlation vs Regression.mp412.72MB
3. Linear Regression with StatsModels/7. Geometrical Representation.mp44.92MB
3. Linear Regression with StatsModels/9. Python Packages Installation.mp436.84MB
4. Linear Regression with Sklearn/1. What is sklearn.mp427.26MB
4. Linear Regression with Sklearn/10. Feature Selection through p-values (F-regression).mp429.54MB
4. Linear Regression with Sklearn/12. Creating a Summary Table with the p-values.mp412.3MB
4. Linear Regression with Sklearn/14. Feature Scaling.mp439.06MB
4. Linear Regression with Sklearn/15. Feature Selection through Standardization.mp434.89MB
4. Linear Regression with Sklearn/16. Making Predictions with Standardized Coefficients.mp425.98MB
4. Linear Regression with Sklearn/18. Underfitting and Overfitting.mp416.96MB
4. Linear Regression with Sklearn/19. Training and Testing.mp449.16MB
4. Linear Regression with Sklearn/2. Game Plan for sklearn.mp419.38MB
4. Linear Regression with Sklearn/3. Simple Linear Regression with sklearn.mp434.77MB
4. Linear Regression with Sklearn/4. Simple Linear Regression with sklearn - Summary Table.mp432MB
4. Linear Regression with Sklearn/7. Multiple Linear Regression with sklearn.mp420.07MB
4. Linear Regression with Sklearn/8. Adjusted R-Squared.mp430.89MB
5. Linear Regression - Practical Example/1. Practical Example (Part 1).mp497.08MB
5. Linear Regression - Practical Example/2. Practical Example (Part 2).mp445.96MB
5. Linear Regression - Practical Example/4. Practical Example (Part 3).mp423.71MB
5. Linear Regression - Practical Example/6. Practical Example (Part 4).mp456.02MB
5. Linear Regression - Practical Example/8. Practical Example (Part 5).mp457.88MB
6. Logistic Regression/1. Introduction to Logistic Regression.mp423.44MB
6. Logistic Regression/10. Dummies in a Logistic Regression.mp435.57MB
6. Logistic Regression/12. Assessing the Accuracy of a Classification Model.mp429.53MB
6. Logistic Regression/14. Underfitting and Overfitting.mp420.38MB
6. Logistic Regression/15. Testing our Model and Bulding a Confusion Matrix.mp430.53MB
6. Logistic Regression/2. A Simple Example of a Logistic Regression in Python.mp432.13MB
6. Logistic Regression/3. What is the Difference Between a Logistic and a Logit Function.mp475.84MB
6. Logistic Regression/4. Your First Logistic Regression.mp415.96MB
6. Logistic Regression/6. A Coding Tip (optional).mp421.07MB
6. Logistic Regression/7. Going through the Regression Summary Table.mp427.81MB
6. Logistic Regression/9. Interpreting the Odds Ratio.mp428.91MB
7. Cluster Analysis/1. Introduction to Cluster Analysis.mp444.62MB
7. Cluster Analysis/10. The Elbow Method or How to Choose the Number of Clusters.mp440.33MB
7. Cluster Analysis/12. Pros and Cons of K-Means.mp432.64MB
7. Cluster Analysis/13. Standardization of Features when Clustering.mp426.83MB
7. Cluster Analysis/14. Cluster Analysis and Regression Analysis.mp49.19MB
7. Cluster Analysis/15. Practical Example Market Segmentation (Part 1).mp440.22MB
7. Cluster Analysis/16. Practical Example Market Segmentation (Part 2).mp450.81MB
7. Cluster Analysis/17. What Can be Done with Cluster Analysis.mp463.54MB
7. Cluster Analysis/2. Examples of Clustering.mp462.93MB
7. Cluster Analysis/3. Classification vs Clustering.mp430.39MB
7. Cluster Analysis/4. Math Concepts Needed to Proceed.mp413.46MB
7. Cluster Analysis/5. K-Means Clustering.mp424.97MB
7. Cluster Analysis/6. A Hands on Example of K-Means.mp448.64MB
7. Cluster Analysis/8. Categorical Data in Cluster Analysis.mp420.25MB
8. Cluster Analysis Additional Topics/1. Other Types of Clustering.mp437.11MB
8. Cluster Analysis Additional Topics/2. The Dendrogram.mp426.78MB
8. Cluster Analysis Additional Topics/3. Heatmaps.mp427.39MB