Regression Analysis in R for Machine Learning & Data Science

1. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/005 Lab_ Machine Learning Models' Comparison & Best Model Selection.mp4101.23MB
2. 01 Introduction to the course, Machine Learning & Regression Analysis/002 Introduction to Regression Analysis.mp449.11MB
3. 01 Introduction to the course, Machine Learning & Regression Analysis/003 What is Machine Leraning and it's main types_.mp434.34MB
4. 01 Introduction to the course, Machine Learning & Regression Analysis/004 Overview of Machine Leraning in R.mp45.66MB
5. 02 Software used in this course R-Studio and Introduction to R/001 Introduction to Section 2.mp43.75MB
6. 02 Software used in this course R-Studio and Introduction to R/002 What is R and RStudio_.mp412.23MB
7. 02 Software used in this course R-Studio and Introduction to R/003 How to install R and RStudio in 2020.mp416.67MB
8. 02 Software used in this course R-Studio and Introduction to R/004 Lab_ Install R and RStudio in 2020.mp438.67MB
9. 02 Software used in this course R-Studio and Introduction to R/005 Introduction to RStudio Interface.mp430.69MB
10. 02 Software used in this course R-Studio and Introduction to R/006 Lab_ Get started with R in RStudio.mp447.7MB
11. 03 R Crash Course - get started with R-programming in R-Studio/001 Introduction to Section 3.mp43.96MB
12. 03 R Crash Course - get started with R-programming in R-Studio/002 Lab_ Installing Packages and Package Management in R.mp424.15MB
13. 03 R Crash Course - get started with R-programming in R-Studio/003 Variables in R and assigning Variables in R.mp48.96MB
14. 03 R Crash Course - get started with R-programming in R-Studio/004 Lab_ Variables in R and assigning Variables in R.mp47.65MB
15. 03 R Crash Course - get started with R-programming in R-Studio/005 Overview of data types and data structures in R.mp427.2MB
16. 03 R Crash Course - get started with R-programming in R-Studio/006 Lab_ data types and data structures in R.mp448.1MB
17. 03 R Crash Course - get started with R-programming in R-Studio/007 Vectors' operations in R.mp435.95MB
18. 03 R Crash Course - get started with R-programming in R-Studio/008 Data types and data structures_ Factors.mp49.32MB
19. 03 R Crash Course - get started with R-programming in R-Studio/009 Dataframes_ overview.mp416.67MB
20. 03 R Crash Course - get started with R-programming in R-Studio/010 Functions in R - overview.mp424.81MB
21. 03 R Crash Course - get started with R-programming in R-Studio/011 Lab_ For Loops in R.mp424.81MB
22. 03 R Crash Course - get started with R-programming in R-Studio/012 Read Data into R.mp431.9MB
23. 04 Linear Regression Analysis for Supervised Machine Learning in R/001 Overview of Regression Analysis.mp449.16MB
24. 04 Linear Regression Analysis for Supervised Machine Learning in R/002 Graphical Analysis of Regression Models.mp416.09MB
25. 04 Linear Regression Analysis for Supervised Machine Learning in R/003 Your first linear regression model in R.mp453.3MB
26. 04 Linear Regression Analysis for Supervised Machine Learning in R/004 Lab_ Correlation & Linear Regression Analysis in R.mp413.06MB
27. 04 Linear Regression Analysis for Supervised Machine Learning in R/005 How to know if the model is best fit for your data - theory.mp49.13MB
28. 04 Linear Regression Analysis for Supervised Machine Learning in R/006 Lab_ Linear Regression Diagnostics.mp443.21MB
29. 04 Linear Regression Analysis for Supervised Machine Learning in R/007 Lab how to measure the linear model's fit_ AIC and BIC.mp48.57MB
30. 04 Linear Regression Analysis for Supervised Machine Learning in R/008 Evaluation of Prediction Model Performance in Supervised Learning_ Regression.mp46.74MB
31. 04 Linear Regression Analysis for Supervised Machine Learning in R/009 Predict with linear regression model & RMSE as in-sample error.mp424.36MB
32. 04 Linear Regression Analysis for Supervised Machine Learning in R/010 Prediction model evaluation with data split_ out-of-sample RMSE.mp431.16MB
33. 05 More types of regression models/001 Lab_ Multiple linear regression - model estimation.mp460.14MB
34. 05 More types of regression models/002 Lab_ Multiple linear regression - prediction.mp418.83MB
35. 05 More types of regression models/003 Lab_ Multiple linear regression with interaction.mp444.54MB
36. 05 More types of regression models/004 Regression with Categorical Variables_ Dummy Coding Essentials in R.mp429.68MB
37. 05 More types of regression models/005 ANOVA - Categorical variables with more than two levels in linear regressions.mp454.54MB
38. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/001 Nonlinear Regression Essentials in R_ Polynomial and Spline Regression Models.mp426.04MB
39. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/002 Lab_ Polynomial regression in R.mp464.94MB
40. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/003 Lab_ Log transformation in R.mp419MB
41. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/004 Lab_ Spline regression in R.mp446.96MB
42. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/005 Lab_ Generalized additive models in R.mp447.49MB
43. 06 Non-Linear Regression Analysis in R_ Polynomial & Spline regression, GAMs/040 034_PolyRegression_LogTransform.R2.75KB
44. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/001 Classification and Decision Trees (CART)_ Theory.mp413.34MB
45. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/002 Lab_ Decision Trees in R.mp451.96MB
46. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/003 Random Forest_ Theory.mp421.26MB
47. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/004 Lab_ Random Forest in R.mp4100.14MB
48. 01 Introduction to the course, Machine Learning & Regression Analysis/001 Introduction.mp421.43MB
49. 07 Non-Parametric Regression Analysis in R_ Random Forest, Decision Trees and more/006 Your Final Project.mp415.04MB