[UdemyCourseDownloader] Regression Analysis for Statistics & Machine Learning in R

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[UdemyCourseDownloader] Regression Analysis for Statistics & Machine Learning in R

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最近下载：2025-01-03

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文件列表

  • 1. Get Started with Practical Regression Analysis in R/8. Basic Exploratory Data Analysis in R.mp4 50.6 MB
  • 1. Get Started with Practical Regression Analysis in R/5. Reading in Data with R.mp4 44.7 MB
  • 1. Get Started with Practical Regression Analysis in R/6. Data Cleaning with R.mp4 42.5 MB
  • 6. Generalized Linear Models(GLMs)/2. Logistic regression.mp4 41.0 MB
  • 7. Working with Non-Parametric and Non-Linear Data/3. Generalized Additive Models (GAMs) in R.mp4 40.4 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/1. Identify Multicollinearity.mp4 40.0 MB
  • 2. Ordinary Least Square Regression Modelling/10. Multiple Linear regression with Interaction and Dummy Variables.mp4 39.4 MB
  • 4. Variable & Model Selection/5. Evaluate Regression Model Performance.mp4 36.5 MB
  • 4. Variable & Model Selection/2. Select the Most Suitable OLS Regression Model.mp4 34.9 MB
  • 2. Ordinary Least Square Regression Modelling/11. Some Basic Conditions that OLS Models Have to Fulfill.mp4 32.0 MB
  • 5. Dealing With Other Violations of the OLS Regression Models/1. Data Transformations.mp4 30.4 MB
  • 7. Working with Non-Parametric and Non-Linear Data/9. Random Forest(RF).mp4 29.6 MB
  • 6. Generalized Linear Models(GLMs)/3. Logistic Regression for Binary Response Variable.mp4 27.6 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/3. Principal Component Regression in R.mp4 26.7 MB
  • 7. Working with Non-Parametric and Non-Linear Data/7. CART-Regression Trees in R.mp4 26.4 MB
  • 2. Ordinary Least Square Regression Modelling/1. OLS Regression- Theory.mp4 26.1 MB
  • 7. Working with Non-Parametric and Non-Linear Data/2. Polynomial and Non-linear regression.mp4 25.4 MB
  • 1. Get Started with Practical Regression Analysis in R/7. Some More Data Cleaning with R.mp4 24.0 MB
  • 4. Variable & Model Selection/7. Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 23.0 MB
  • 7. Working with Non-Parametric and Non-Linear Data/5. Multivariate Adaptive Regression Splines (MARS).mp4 22.7 MB
  • 2. Ordinary Least Square Regression Modelling/2. OLS-Implementation.mp4 22.6 MB
  • 4. Variable & Model Selection/3. Select Model Subsets.mp4 20.5 MB
  • 2. Ordinary Least Square Regression Modelling/6. Confidence Interval and OLS Regressions.mp4 19.5 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/5. Ridge Regression in R.mp4 19.4 MB
  • 1. Get Started with Practical Regression Analysis in R/4. Getting Started with R and R Studio.mp4 19.2 MB
  • 5. Dealing With Other Violations of the OLS Regression Models/3. Dealing with Heteroscedasticity.mp4 19.2 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/4. Partial Least Square Regression in R.mp4 18.2 MB
  • 2. Ordinary Least Square Regression Modelling/3. More on Result Interpretations.mp4 17.9 MB
  • 4. Variable & Model Selection/4. Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4 17.7 MB
  • 5. Dealing With Other Violations of the OLS Regression Models/2. Robust Regression-Deal with Outliers.mp4 17.3 MB
  • 7. Working with Non-Parametric and Non-Linear Data/4. Boosted GAM Regression.mp4 16.6 MB
  • 6. Generalized Linear Models(GLMs)/4. Multinomial Logistic Regression.mp4 16.2 MB
  • 6. Generalized Linear Models(GLMs)/4. Multinomial Logistic Regression.vtt 16.2 MB
  • 7. Working with Non-Parametric and Non-Linear Data/8. Conditional Inference Trees.mp4 15.7 MB
  • 2. Ordinary Least Square Regression Modelling/9. Multiple Linear Regression.mp4 15.7 MB
  • 6. Generalized Linear Models(GLMs)/5. Regression for Count Data.mp4 14.8 MB
  • 2. Ordinary Least Square Regression Modelling/4. Confidence Interval-Theory.mp4 14.4 MB
  • 1. Get Started with Practical Regression Analysis in R/3. Difference Between Statistical Analysis & Machine Learning.mp4 14.4 MB
  • 7. Working with Non-Parametric and Non-Linear Data/11. ML Model Selection.mp4 13.6 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/2. Doing Regression Analyses with Correlated Predictor Variables.mp4 13.4 MB
  • 6. Generalized Linear Models(GLMs)/1. What are GLMs.mp4 12.4 MB
  • 7. Working with Non-Parametric and Non-Linear Data/10. Gradient Boosting Regression.mp4 11.7 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/6. LASSO Regression.mp4 11.4 MB
  • 4. Variable & Model Selection/1. Why Do Any Kind of Selection.mp4 11.1 MB
  • 2. Ordinary Least Square Regression Modelling/5. Calculate the Confidence Interval in R.mp4 10.7 MB
  • 6. Generalized Linear Models(GLMs)/6. Goodness of fit testing.mp4 9.3 MB
  • 4. Variable & Model Selection/6. LASSO Regression for Variable Selection.mp4 8.7 MB
  • 2. Ordinary Least Square Regression Modelling/7. Linear Regression without Intercept.mp4 8.7 MB
  • 1. Get Started with Practical Regression Analysis in R/1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools.mp4 8.2 MB
  • 2. Ordinary Least Square Regression Modelling/8. Implement ANOVA on OLS Regression.mp4 7.7 MB
  • 2. Ordinary Least Square Regression Modelling/12. Conclusions to Section 2.mp4 7.3 MB
  • 6. Generalized Linear Models(GLMs)/7. Conclusions to Section 6.mp4 6.1 MB
  • 3. Deal with Multicollinearity in OLS Regression Models/7. Conclusion to Section 3.mp4 5.5 MB
  • 1. Get Started with Practical Regression Analysis in R/9. Conclusion to Section 1.mp4 4.9 MB
  • 7. Working with Non-Parametric and Non-Linear Data/12. Conclusions to Section 7.mp4 4.3 MB
  • 4. Variable & Model Selection/8. Conclusions to Section 4.mp4 4.1 MB
  • 5. Dealing With Other Violations of the OLS Regression Models/4. Conclusions to Section 5.mp4 3.0 MB
  • 1. Get Started with Practical Regression Analysis in R/Regression Analysis_Data and Scripts.zip 957.3 kB
  • 1. Get Started with Practical Regression Analysis in R/8. Basic Exploratory Data Analysis in R.vtt 19.5 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/1. Identify Multicollinearity.vtt 17.1 kB
  • 6. Generalized Linear Models(GLMs)/2. Logistic regression.vtt 16.5 kB
  • 1. Get Started with Practical Regression Analysis in R/6. Data Cleaning with R.vtt 16.4 kB
  • 2. Ordinary Least Square Regression Modelling/10. Multiple Linear regression with Interaction and Dummy Variables.vtt 16.3 kB
  • 4. Variable & Model Selection/5. Evaluate Regression Model Performance.vtt 15.7 kB
  • 1. Get Started with Practical Regression Analysis in R/5. Reading in Data with R.vtt 15.5 kB
  • 2. Ordinary Least Square Regression Modelling/11. Some Basic Conditions that OLS Models Have to Fulfill.vtt 13.8 kB
  • 7. Working with Non-Parametric and Non-Linear Data/3. Generalized Additive Models (GAMs) in R.vtt 13.5 kB
  • 4. Variable & Model Selection/2. Select the Most Suitable OLS Regression Model.vtt 13.0 kB
  • 5. Dealing With Other Violations of the OLS Regression Models/1. Data Transformations.vtt 12.7 kB
  • 7. Working with Non-Parametric and Non-Linear Data/7. CART-Regression Trees in R.vtt 12.5 kB
  • 7. Working with Non-Parametric and Non-Linear Data/9. Random Forest(RF).vtt 12.2 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/3. Principal Component Regression in R.vtt 11.5 kB
  • 2. Ordinary Least Square Regression Modelling/1. OLS Regression- Theory.vtt 10.9 kB
  • 7. Working with Non-Parametric and Non-Linear Data/2. Polynomial and Non-linear regression.vtt 10.4 kB
  • 6. Generalized Linear Models(GLMs)/3. Logistic Regression for Binary Response Variable.vtt 9.7 kB
  • 4. Variable & Model Selection/3. Select Model Subsets.vtt 9.5 kB
  • 2. Ordinary Least Square Regression Modelling/2. OLS-Implementation.vtt 9.3 kB
  • 2. Ordinary Least Square Regression Modelling/3. More on Result Interpretations.vtt 9.2 kB
  • 7. Working with Non-Parametric and Non-Linear Data/5. Multivariate Adaptive Regression Splines (MARS).vtt 9.1 kB
  • 1. Get Started with Practical Regression Analysis in R/7. Some More Data Cleaning with R.vtt 8.9 kB
  • 4. Variable & Model Selection/7. Identify the Contribution of Predictors in Explaining the Variation in Y.vtt 8.8 kB
  • 2. Ordinary Least Square Regression Modelling/6. Confidence Interval and OLS Regressions.vtt 8.3 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/4. Partial Least Square Regression in R.vtt 8.0 kB
  • 4. Variable & Model Selection/4. Machine Learning Perspective on Evaluate Regression Model Accuracy.vtt 8.0 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/5. Ridge Regression in R.vtt 7.9 kB
  • 5. Dealing With Other Violations of the OLS Regression Models/3. Dealing with Heteroscedasticity.vtt 7.3 kB
  • 2. Ordinary Least Square Regression Modelling/9. Multiple Linear Regression.vtt 7.2 kB
  • 5. Dealing With Other Violations of the OLS Regression Models/2. Robust Regression-Deal with Outliers.vtt 7.2 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/2. Doing Regression Analyses with Correlated Predictor Variables.vtt 6.7 kB
  • 1. Get Started with Practical Regression Analysis in R/4. Getting Started with R and R Studio.vtt 6.7 kB
  • 1. Get Started with Practical Regression Analysis in R/3. Difference Between Statistical Analysis & Machine Learning.vtt 6.5 kB
  • 2. Ordinary Least Square Regression Modelling/4. Confidence Interval-Theory.vtt 6.4 kB
  • 6. Generalized Linear Models(GLMs)/5. Regression for Count Data.vtt 6.3 kB
  • 7. Working with Non-Parametric and Non-Linear Data/8. Conditional Inference Trees.vtt 6.3 kB
  • 7. Working with Non-Parametric and Non-Linear Data/11. ML Model Selection.vtt 6.2 kB
  • 7. Working with Non-Parametric and Non-Linear Data/4. Boosted GAM Regression.vtt 5.7 kB
  • 4. Variable & Model Selection/1. Why Do Any Kind of Selection.vtt 5.7 kB
  • 6. Generalized Linear Models(GLMs)/1. What are GLMs.vtt 5.5 kB
  • 2. Ordinary Least Square Regression Modelling/5. Calculate the Confidence Interval in R.vtt 5.1 kB
  • 7. Working with Non-Parametric and Non-Linear Data/10. Gradient Boosting Regression.vtt 4.6 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/6. LASSO Regression.vtt 4.6 kB
  • 4. Variable & Model Selection/6. LASSO Regression for Variable Selection.vtt 4.2 kB
  • 6. Generalized Linear Models(GLMs)/6. Goodness of fit testing.vtt 4.2 kB
  • 2. Ordinary Least Square Regression Modelling/7. Linear Regression without Intercept.vtt 4.0 kB
  • 2. Ordinary Least Square Regression Modelling/8. Implement ANOVA on OLS Regression.vtt 3.8 kB
  • 2. Ordinary Least Square Regression Modelling/12. Conclusions to Section 2.vtt 3.5 kB
  • 1. Get Started with Practical Regression Analysis in R/9. Conclusion to Section 1.vtt 2.5 kB
  • 6. Generalized Linear Models(GLMs)/7. Conclusions to Section 6.vtt 2.4 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/7. Conclusion to Section 3.vtt 2.3 kB
  • 7. Working with Non-Parametric and Non-Linear Data/3.1 gam.txt.txt 2.1 kB
  • 7. Working with Non-Parametric and Non-Linear Data/12. Conclusions to Section 7.vtt 2.0 kB
  • 4. Variable & Model Selection/8. Conclusions to Section 4.vtt 1.9 kB
  • 1. Get Started with Practical Regression Analysis in R/1. INTRODUCTION TO THE COURSE The Key Concepts and Software Tools.vtt 1.8 kB
  • 3. Deal with Multicollinearity in OLS Regression Models/1.1 Lecture21_multicol1.txt.txt 1.8 kB
  • 7. Working with Non-Parametric and Non-Linear Data/4.1 bgam.txt.txt 1.5 kB
  • 5. Dealing With Other Violations of the OLS Regression Models/4. Conclusions to Section 5.vtt 1.3 kB
  • 1. Get Started with Practical Regression Analysis in R/8.1 EDA.txt.txt 1.1 kB
  • 7. Working with Non-Parametric and Non-Linear Data/1. Work With Non-Parametric and Non-Linear Data.html 669 Bytes
  • 7. Working with Non-Parametric and Non-Linear Data/6. Machine Learning Regression-Tree Based Methods.html 468 Bytes
  • 1. Get Started with Practical Regression Analysis in R/2. Data For the Course.html 151 Bytes
  • udemycoursedownloader.com.url 132 Bytes
  • Udemy Course downloader.txt 94 Bytes

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