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

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

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1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4

115.63MB

1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp4

72.07MB

1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp4

22.2MB

1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp4

49.83MB

1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp4

44.79MB

1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp4

28.97MB

1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp4

55.59MB

1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp4

5.33MB

2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp4

27.72MB

2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp4

25.54MB

2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp4

18.01MB

2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp4

14.98MB

2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp4

8.12MB

2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp4

21.31MB

2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp4

9.17MB

2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp4

7.48MB

2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp4

17.19MB

2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp4

30.27MB

2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp4

27.59MB

2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp4

7.97MB

3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp4

28.71MB

3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp4

14.29MB

3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp4

29.6MB

3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp4

19.58MB

3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp4

20.94MB

3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp4

12.58MB

3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp4

6.05MB

4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp4

11.61MB

4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp4

38.77MB

4.Variable & Model Selection/30.Select Model Subsets.mp4

21.11MB

4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4

19.43MB

4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp4

39.65MB

4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp4

9.08MB

4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4

24.88MB

4.Variable & Model Selection/35.Conclusions to Section 4.mp4

4.46MB

5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp4

23.11MB

5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp4

19.1MB

5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp4

14.89MB

5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp4

3.44MB

6.Generalized Linear Models (GLMs)/40.What are GLMs.mp4

12.71MB

6.Generalized Linear Models (GLMs)/41.Logistic regression.mp4

44.41MB

6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp4

31.67MB

6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp4

18.2MB

6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp4

16.06MB

6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp4

87.21MB

6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp4

6.73MB

7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp4

18.86MB

7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp4

39.93MB

7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp4

16.5MB

7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp4

26.45MB

7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp4

28.33MB

7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp4

11.71MB

7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp4

20.49MB

7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp4

8.61MB

7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4

102.18MB

7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp4

24.93MB

Exercise Files/code_9781838987862.zip

27.97MB

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