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

1. 1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4115.63MB
2. 1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp472.07MB
3. 1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp422.2MB
4. 1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp449.83MB
5. 1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp444.79MB
6. 1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp428.97MB
7. 1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp455.59MB
8. 1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp45.33MB
9. 2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp427.72MB
10. 2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp425.54MB
11. 2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp418.01MB
12. 2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp414.98MB
13. 2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp48.12MB
14. 2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp421.31MB
15. 2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp49.17MB
16. 2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp47.48MB
17. 2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp417.19MB
18. 2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp430.27MB
19. 2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp427.59MB
20. 2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp47.97MB
21. 3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp428.71MB
22. 3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp414.29MB
23. 3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp429.6MB
24. 3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp419.58MB
25. 3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp420.94MB
26. 3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp412.58MB
27. 3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp46.05MB
28. 4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp411.61MB
29. 4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp438.77MB
30. 4.Variable & Model Selection/30.Select Model Subsets.mp421.11MB
31. 4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp419.43MB
32. 4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp439.65MB
33. 4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp49.08MB
34. 4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp424.88MB
35. 4.Variable & Model Selection/35.Conclusions to Section 4.mp44.46MB
36. 5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp423.11MB
37. 5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp419.1MB
38. 5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp414.89MB
39. 5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp43.44MB
40. 6.Generalized Linear Models (GLMs)/40.What are GLMs.mp412.71MB
41. 6.Generalized Linear Models (GLMs)/41.Logistic regression.mp444.41MB
42. 6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp431.67MB
43. 6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp418.2MB
44. 6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp416.06MB
45. 6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp487.21MB
46. 6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp46.73MB
47. 7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp418.86MB
48. 7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp439.93MB
49. 7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp416.5MB
50. 7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp426.45MB
51. 7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp428.33MB
52. 7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp411.71MB
53. 7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp420.49MB
54. 7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp48.61MB
55. 7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4102.18MB
56. 7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp424.93MB