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种子名称： [FreeCoursesOnline.Me] [Packt] Regression Analysis for Statistics and Machine Learning in R [FCO]
文件类型： 视频
文件数目： 56个文件
文件大小： 1.45 GB
收录时间： 2020-1-21 05:15
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最近下载： 2025-3-3 18:50

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[FreeCoursesOnline.Me] [Packt] Regression Analysis for Statistics and Machine Learning in R [FCO].torrent

1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4115.63MB

1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp472.07MB

1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp422.2MB

1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp449.83MB

1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp444.79MB

1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp428.97MB

1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp455.59MB

1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp45.33MB

2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp427.72MB

2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp425.54MB

2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp418.01MB

2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp414.98MB

2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp48.12MB

2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp421.31MB

2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp49.17MB

2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp47.48MB

2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp417.19MB

2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp430.27MB

2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp427.59MB

2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp47.97MB

3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp428.71MB

3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp414.29MB

3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp429.6MB

3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp419.58MB

3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp420.94MB

3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp412.58MB

3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp46.05MB

4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp411.61MB

4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp438.77MB

4.Variable & Model Selection/30.Select Model Subsets.mp421.11MB

4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp419.43MB

4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp439.65MB

4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp49.08MB

4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp424.88MB

4.Variable & Model Selection/35.Conclusions to Section 4.mp44.46MB

5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp423.11MB

5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp419.1MB

5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp414.89MB

5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp43.44MB

6.Generalized Linear Models (GLMs)/40.What are GLMs.mp412.71MB

6.Generalized Linear Models (GLMs)/41.Logistic regression.mp444.41MB

6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp431.67MB

6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp418.2MB

6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp416.06MB

6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp487.21MB

6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp46.73MB

7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp418.86MB

7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp439.93MB

7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp416.5MB

7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp426.45MB

7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp428.33MB

7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp411.71MB

7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp420.49MB

7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp48.61MB

7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4102.18MB

7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp424.93MB