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[] Udemy - Data Science in R Be an Expert in Regression Analysis in R

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文件列表

  1. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/005 Lab_ Machine Learning Models' Comparison & Best Model Selection.mp4 101MB
  2. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/004 Lab_ Random Forest in R.mp4 100MB
  3. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/002 Lab_ Polynomial regression in R.mp4 65MB
  4. 05 More types of regression models/001 Lab_ Multiple linear regression - model estimation.mp4 60MB
  5. 05 More types of regression models/005 ANOVA - Categorical variables with more than two levels in linear regressions.mp4 55MB
  6. 04 Linear Regression Analysis for Supervised Machine Learning in R/003 Your first linear regression model in R.mp4 53MB
  7. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/002 Lab_ Decision Trees in R.mp4 52MB
  8. 04 Linear Regression Analysis for Supervised Machine Learning in R/001 Overview of Regression Analysis.mp4 49MB
  9. 01 Introduction to the course, Machine Learning & Regression Analysis/002 Introduction to Regression Analysis.mp4 49MB
  10. 03 R Crash Course - get started with R-programming in R-Studio/006 Lab_ data types and data structures in R.mp4 48MB
  11. 02 Software used in this course R-Studio and Introduction to R/006 Lab_ Get started with R in RStudio.mp4 48MB
  12. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/005 Lab_ Generalized additive models in R.mp4 47MB
  13. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/004 Lab_ Spline regression in R.mp4 47MB
  14. 05 More types of regression models/003 Lab_ Multiple linear regression with interaction.mp4 45MB
  15. 04 Linear Regression Analysis for Supervised Machine Learning in R/006 Lab_ Linear Regression Diagnostics.mp4 43MB
  16. 02 Software used in this course R-Studio and Introduction to R/004 Lab_ Install R and RStudio in 2020.mp4 39MB
  17. 03 R Crash Course - get started with R-programming in R-Studio/007 Vectors' operations in R.mp4 36MB
  18. 01 Introduction to the course, Machine Learning & Regression Analysis/003 What is Machine Leraning and it's main types_.mp4 34MB
  19. 03 R Crash Course - get started with R-programming in R-Studio/012 Read Data into R.mp4 32MB
  20. 04 Linear Regression Analysis for Supervised Machine Learning in R/010 Prediction model evaluation with data split_ out-of-sample RMSE.mp4 31MB
  21. 02 Software used in this course R-Studio and Introduction to R/005 Introduction to RStudio Interface.mp4 31MB
  22. 05 More types of regression models/004 Regression with Categorical Variables_ Dummy Coding Essentials in R.mp4 30MB
  23. 03 R Crash Course - get started with R-programming in R-Studio/005 Overview of data types and data structures in R.mp4 27MB
  24. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/001 Nonlinear Regression Essentials in R_ Polynomial and Spline Regression Models.mp4 26MB
  25. 03 R Crash Course - get started with R-programming in R-Studio/011 Lab_ For Loops in R.mp4 25MB
  26. 03 R Crash Course - get started with R-programming in R-Studio/010 Functions in R - overview.mp4 25MB
  27. 04 Linear Regression Analysis for Supervised Machine Learning in R/009 Predict with linear regression model & RMSE as in-sample error.mp4 24MB
  28. 03 R Crash Course - get started with R-programming in R-Studio/002 Lab_ Installing Packages and Package Management in R.mp4 24MB
  29. 01 Introduction to the course, Machine Learning & Regression Analysis/001 Introduction.mp4 21MB
  30. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/003 Random Forest_ Theory.mp4 21MB
  31. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/003 Lab_ Log transformation in R.mp4 19MB
  32. 05 More types of regression models/002 Lab_ Multiple linear regression - prediction.mp4 19MB
  33. 02 Software used in this course R-Studio and Introduction to R/003 How to install R and RStudio in 2020.mp4 17MB
  34. 03 R Crash Course - get started with R-programming in R-Studio/009 Dataframes_ overview.mp4 17MB
  35. 04 Linear Regression Analysis for Supervised Machine Learning in R/002 Graphical Analysis of Regression Models.mp4 16MB
  36. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/006 Your Final Project.mp4 15MB
  37. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/001 Classification and Decision Trees (CART)_ Theory.mp4 13MB
  38. 04 Linear Regression Analysis for Supervised Machine Learning in R/004 Lab_ Correlation & Linear Regression Analysis in R.mp4 13MB
  39. 02 Software used in this course R-Studio and Introduction to R/002 What is R and RStudio_.mp4 12MB
  40. 03 R Crash Course - get started with R-programming in R-Studio/008 Data types and data structures_ Factors.mp4 9MB
  41. 04 Linear Regression Analysis for Supervised Machine Learning in R/005 How to know if the model is best fit for your data - theory.mp4 9MB
  42. 03 R Crash Course - get started with R-programming in R-Studio/003 Variables in R and assigning Variables in R.mp4 9MB
  43. 04 Linear Regression Analysis for Supervised Machine Learning in R/007 Lab how to measure the linear model's fit_ AIC and BIC.mp4 9MB
  44. 03 R Crash Course - get started with R-programming in R-Studio/004 Lab_ Variables in R and assigning Variables in R.mp4 8MB
  45. 04 Linear Regression Analysis for Supervised Machine Learning in R/008 Evaluation of Prediction Model Performance in Supervised Learning_ Regression.mp4 7MB
  46. 01 Introduction to the course, Machine Learning & Regression Analysis/004 Overview of Machine Leraning in R.mp4 6MB
  47. 03 R Crash Course - get started with R-programming in R-Studio/001 Introduction to Section 3.mp4 4MB
  48. 02 Software used in this course R-Studio and Introduction to R/001 Introduction to Section 2.mp4 4MB
  49. 03 R Crash Course - get started with R-programming in R-Studio/011 R Crash Course I_udemy_script.R 13KB
  50. 05 More types of regression models/033 029_MultipleLinearRegression.R 4KB
  51. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/047 027_ModelCompare.R 3KB
  52. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/040 034_PolyRegression_LogTransform.R 3KB
  53. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/041 035_SplineRegression.R 2KB
  54. 04 Linear Regression Analysis for Supervised Machine Learning in R/025 018_LM_diamonds.R 2KB
  55. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/039 033_PolynomialRegression.R 2KB
  56. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/046 026_RandomForest.R 2KB
  57. 05 More types of regression models/035 030_MultipleLinearRegression_interactions.R 1KB
  58. 04 Linear Regression Analysis for Supervised Machine Learning in R/028 020_LM_Diagnosis.R 1KB
  59. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/044 025_DecisionTress.R 1KB
  60. 05 More types of regression models/037 032_ANOVA.R 1KB
  61. 05 More types of regression models/036 031_DummyVariables.R 1KB
  62. 04 Linear Regression Analysis for Supervised Machine Learning in R/032 022_RegressionModelValidation.R 875B
  63. 04 Linear Regression Analysis for Supervised Machine Learning in R/031 019_RMSE_LM.R 827B
  64. 04 Linear Regression Analysis for Supervised Machine Learning in R/026 020_CorrelationLinear.R 785B
  65. 04 Linear Regression Analysis for Supervised Machine Learning in R/029 021_AIC.R 543B
  66. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/042 036_GAM.R 441B
  67. 0. Websites you may like/[GigaCourse.Com].url 49B
  68. 04 Linear Regression Analysis for Supervised Machine Learning in R/[GigaCourse.Com].url 49B
  69. [GigaCourse.Com].url 49B