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

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

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种子哈希：46ccad14c58fc19493e0b01ec226202b0d9cc1c0
文件大小： 1.48G
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收录时间：2021-03-09
最近下载：2024-11-12

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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 121.2 MB
  • 7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4 107.1 MB
  • 6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp4 91.4 MB
  • 1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp4 75.6 MB
  • 1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp4 58.3 MB
  • 1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp4 52.2 MB
  • 1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp4 47.0 MB
  • 6.Generalized Linear Models (GLMs)/41.Logistic regression.mp4 46.6 MB
  • 7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp4 41.9 MB
  • 4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp4 41.6 MB
  • 4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp4 40.7 MB
  • 6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp4 33.2 MB
  • 2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp4 31.7 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp4 31.0 MB
  • 1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp4 30.4 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp4 30.1 MB
  • 7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp4 29.7 MB
  • Exercise Files/code_9781838987862.zip 29.3 MB
  • 2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp4 29.1 MB
  • 2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp4 28.9 MB
  • 7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp4 27.7 MB
  • 2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp4 26.8 MB
  • 7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp4 26.1 MB
  • 4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 26.1 MB
  • 5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp4 24.2 MB
  • 1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp4 23.3 MB
  • 2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp4 22.3 MB
  • 4.Variable & Model Selection/30.Select Model Subsets.mp4 22.1 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp4 22.0 MB
  • 7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp4 21.5 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp4 20.5 MB
  • 4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4 20.4 MB
  • 5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp4 20.0 MB
  • 7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp4 19.8 MB
  • 6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp4 19.1 MB
  • 2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp4 18.9 MB
  • 2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp4 18.0 MB
  • 7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp4 17.3 MB
  • 6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp4 16.8 MB
  • 2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp4 15.7 MB
  • 5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp4 15.6 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp4 15.0 MB
  • 6.Generalized Linear Models (GLMs)/40.What are GLMs.mp4 13.3 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp4 13.2 MB
  • 7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp4 12.3 MB
  • 4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp4 12.2 MB
  • 2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp4 9.6 MB
  • 4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp4 9.5 MB
  • 7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp4 9.0 MB
  • 2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp4 8.5 MB
  • 2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp4 8.4 MB
  • 2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp4 7.8 MB
  • 6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp4 7.1 MB
  • 3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp4 6.3 MB
  • 1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp4 5.6 MB
  • 4.Variable & Model Selection/35.Conclusions to Section 4.mp4 4.7 MB
  • 5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp4 3.6 MB
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