[] [Packt] Regression Analysis for Statistics and Machine Learning in R [FCO]
- 收录时间:2021-06-07 21:18:58
- 文件大小:1GB
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- 最近下载:2021-06-07 21:18:58
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文件列表
- 1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4 116MB
- 7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4 102MB
- 6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp4 87MB
- 1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp4 72MB
- 1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp4 56MB
- 1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp4 50MB
- 1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp4 45MB
- 6.Generalized Linear Models (GLMs)/41.Logistic regression.mp4 44MB
- 7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp4 40MB
- 4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp4 40MB
- 4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp4 39MB
- 6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp4 32MB
- 2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp4 30MB
- 3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp4 30MB
- 1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp4 29MB
- 3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp4 29MB
- 7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp4 28MB
- Exercise Files/code_9781838987862.zip 28MB
- 2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp4 28MB
- 2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp4 28MB
- 7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp4 26MB
- 2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp4 26MB
- 7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp4 25MB
- 4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 25MB
- 5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp4 23MB
- 1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp4 22MB
- 2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp4 21MB
- 4.Variable & Model Selection/30.Select Model Subsets.mp4 21MB
- 3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp4 21MB
- 7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp4 20MB
- 3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp4 20MB
- 4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4 19MB
- 5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp4 19MB
- 7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp4 19MB
- 6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp4 18MB
- 2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp4 18MB
- 2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp4 17MB
- 7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp4 16MB
- 6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp4 16MB
- 2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp4 15MB
- 5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp4 15MB
- 3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp4 14MB
- 6.Generalized Linear Models (GLMs)/40.What are GLMs.mp4 13MB
- 3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp4 13MB
- 7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp4 12MB
- 4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp4 12MB
- 2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp4 9MB
- 4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp4 9MB
- 7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp4 9MB
- 2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp4 8MB
- 2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp4 8MB
- 2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp4 7MB
- 6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp4 7MB
- 3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp4 6MB
- 1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp4 5MB
- 4.Variable & Model Selection/35.Conclusions to Section 4.mp4 4MB
- 5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp4 3MB
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