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07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/005 Lab_ Machine Learning Models' Comparison & Best Model Selection.mp4 106.2 MB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/004 Lab_ Random Forest in R.mp4 105.0 MB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/002 Lab_ Polynomial regression in R.mp4 68.1 MB
05 More types of regression models/001 Lab_ Multiple linear regression - model estimation.mp4 63.1 MB
05 More types of regression models/005 ANOVA - Categorical variables with more than two levels in linear regressions.mp4 57.2 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/003 Your first linear regression model in R.mp4 55.9 MB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/002 Lab_ Decision Trees in R.mp4 54.5 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/001 Overview of Regression Analysis.mp4 51.5 MB
01 Introduction to the course, Machine Learning & Regression Analysis/002 Introduction to Regression Analysis.mp4 51.5 MB
03 R Crash Course - get started with R-programming in R-Studio/006 Lab_ data types and data structures in R.mp4 50.4 MB
02 Software used in this course R-Studio and Introduction to R/006 Lab_ Get started with R in RStudio.mp4 50.0 MB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/005 Lab_ Generalized additive models in R.mp4 49.8 MB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/004 Lab_ Spline regression in R.mp4 49.2 MB
05 More types of regression models/003 Lab_ Multiple linear regression with interaction.mp4 46.7 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/006 Lab_ Linear Regression Diagnostics.mp4 45.3 MB
02 Software used in this course R-Studio and Introduction to R/004 Lab_ Install R and RStudio in 2020.mp4 40.6 MB
03 R Crash Course - get started with R-programming in R-Studio/007 Vectors' operations in R.mp4 37.7 MB
01 Introduction to the course, Machine Learning & Regression Analysis/003 What is Machine Leraning and it's main types_.mp4 36.0 MB
03 R Crash Course - get started with R-programming in R-Studio/012 Read Data into R.mp4 33.5 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/010 Prediction model evaluation with data split_ out-of-sample RMSE.mp4 32.7 MB
02 Software used in this course R-Studio and Introduction to R/005 Introduction to RStudio Interface.mp4 32.2 MB
05 More types of regression models/004 Regression with Categorical Variables_ Dummy Coding Essentials in R.mp4 31.1 MB
03 R Crash Course - get started with R-programming in R-Studio/005 Overview of data types and data structures in R.mp4 28.5 MB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/001 Nonlinear Regression Essentials in R_ Polynomial and Spline Regression Models.mp4 27.3 MB
03 R Crash Course - get started with R-programming in R-Studio/011 Lab_ For Loops in R.mp4 26.0 MB
03 R Crash Course - get started with R-programming in R-Studio/010 Functions in R - overview.mp4 26.0 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/009 Predict with linear regression model & RMSE as in-sample error.mp4 25.5 MB
03 R Crash Course - get started with R-programming in R-Studio/002 Lab_ Installing Packages and Package Management in R.mp4 25.3 MB
01 Introduction to the course, Machine Learning & Regression Analysis/001 Introduction.mp4 22.5 MB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/003 Random Forest_ Theory.mp4 22.3 MB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/003 Lab_ Log transformation in R.mp4 19.9 MB
05 More types of regression models/002 Lab_ Multiple linear regression - prediction.mp4 19.7 MB
02 Software used in this course R-Studio and Introduction to R/003 How to install R and RStudio in 2020.mp4 17.5 MB
03 R Crash Course - get started with R-programming in R-Studio/009 Dataframes_ overview.mp4 17.5 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/002 Graphical Analysis of Regression Models.mp4 16.9 MB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/006 Your Final Project.mp4 15.8 MB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/001 Classification and Decision Trees (CART)_ Theory.mp4 14.0 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/004 Lab_ Correlation & Linear Regression Analysis in R.mp4 13.7 MB
02 Software used in this course R-Studio and Introduction to R/002 What is R and RStudio_.mp4 12.8 MB
03 R Crash Course - get started with R-programming in R-Studio/008 Data types and data structures_ Factors.mp4 9.8 MB
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 9.6 MB
03 R Crash Course - get started with R-programming in R-Studio/003 Variables in R and assigning Variables in R.mp4 9.4 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/007 Lab how to measure the linear model's fit_ AIC and BIC.mp4 9.0 MB
03 R Crash Course - get started with R-programming in R-Studio/004 Lab_ Variables in R and assigning Variables in R.mp4 8.0 MB
04 Linear Regression Analysis for Supervised Machine Learning in R/008 Evaluation of Prediction Model Performance in Supervised Learning_ Regression.mp4 7.1 MB
01 Introduction to the course, Machine Learning & Regression Analysis/004 Overview of Machine Leraning in R.mp4 5.9 MB
03 R Crash Course - get started with R-programming in R-Studio/001 Introduction to Section 3.mp4 4.2 MB
02 Software used in this course R-Studio and Introduction to R/001 Introduction to Section 2.mp4 3.9 MB
03 R Crash Course - get started with R-programming in R-Studio/011 R Crash Course I_udemy_script.R 13.2 kB
05 More types of regression models/033 029_MultipleLinearRegression.R 3.9 kB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/047 027_ModelCompare.R 3.1 kB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/040 034_PolyRegression_LogTransform.R 2.8 kB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/041 035_SplineRegression.R 2.3 kB
04 Linear Regression Analysis for Supervised Machine Learning in R/025 018_LM_diamonds.R 2.3 kB
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/039 033_PolynomialRegression.R 2.1 kB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/046 026_RandomForest.R 1.8 kB
05 More types of regression models/035 030_MultipleLinearRegression_interactions.R 1.5 kB
04 Linear Regression Analysis for Supervised Machine Learning in R/028 020_LM_Diagnosis.R 1.4 kB
07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/044 025_DecisionTress.R 1.2 kB
05 More types of regression models/037 032_ANOVA.R 1.2 kB
05 More types of regression models/036 031_DummyVariables.R 1.1 kB
04 Linear Regression Analysis for Supervised Machine Learning in R/032 022_RegressionModelValidation.R 875 Bytes
04 Linear Regression Analysis for Supervised Machine Learning in R/031 019_RMSE_LM.R 827 Bytes
04 Linear Regression Analysis for Supervised Machine Learning in R/026 020_CorrelationLinear.R 785 Bytes
04 Linear Regression Analysis for Supervised Machine Learning in R/029 021_AIC.R 543 Bytes
06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/042 036_GAM.R 441 Bytes
0. Websites you may like/[GigaCourse.Com].url 49 Bytes
04 Linear Regression Analysis for Supervised Machine Learning in R/[GigaCourse.Com].url 49 Bytes
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