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Regression Analysis for Statistics & Machine Learning in R
Get Started with Practical Regression Analysis in R
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools (1:15)
Data For the Course
Difference Between Statistical Analysis & Machine Learning (5:36)
Getting Started with R and R Studio (6:36)
Reading in Data with R (15:28)
Data Cleaning with R (17:12)
Some More Data Cleaning with R (8:05)
Basic Exploratory Data Analysis in R (18:54)
Conclusion to Section 1 (1:58)
Ordinary Least Square Regression Modelling
OLS Regression- Theory (10:44)
OLS-Implementation (8:40)
More on Result Interpretations (7:46)
Confidence Interval-Theory (6:06)
Calculate the Confidence Interval in R (4:53)
Confidence Interval and OLS Regressions (7:20)
Linear Regression without Intercept (3:40)
Implement ANOVA on OLS Regression (3:38)
Multiple Linear Regression (6:27)
Multiple Linear regression with Interaction and Dummy Variables (15:05)
Some Basic Conditions that OLS Models Have to Fulfill (12:56)
Conclusions to Section 2 (2:55)
Deal with Multicollinearity in OLS Regression Models
Identify Multicollinearity (16:42)
Doing Regression Analyses with Correlated Predictor Variables (5:36)
Principal Component Regression in R (10:39)
Partial Least Square Regression in R (7:33)
Ridge Regression in R (7:22)
LASSO Regression (4:24)
Conclusion to Section 3 (2:00)
Variable & Model Selection
Why Do Any Kind of Selection? (4:40)
Select the Most Suitable OLS Regression Model (13:19)
Select Model Subsets (8:22)
Machine Learning Perspective on Evaluate Regression Model Accuracy (7:10)
Evaluate Regression Model Performance (14:26)
LASSO Regression for Variable Selection (3:42)
Identify the Contribution of Predictors in Explaining the Variation in Y (8:38)
Conclusions to Section 4 (1:35)
Dealing With Other Violations of the OLS Regression Models
Data Transformations (12:17)
Robust Regression-Deal with Outliers (6:58)
Dealing with Heteroscedasticity (7:13)
Conclusions to Section 5 (1:12)
Generalized Linear Models(GLMs)
What are GLMs? (5:25)
Logistic regression (16:18)
Logistic Regression for Binary Response Variable (9:10)
Multinomial Logistic Regression (6:12)
Regression for Count Data (6:19)
Goodness of fit testing (3:43)
Conclusions to Section 6 (2:12)
Working with Non-Parametric and Non-Linear Data
Polynomial and Non-linear regression (9:45)
Work With Non-Parametric and Non-Linear Data
Generalized Additive Models (GAMs) in R (14:09)
Boosted GAM Regression (6:15)
Multivariate Adaptive Regression Splines (MARS) (8:06)
Machine Learning Regression-Tree Based Methods
CART-Regression Trees in R (10:54)
Conditional Inference Trees (5:46)
Random Forest(RF) (11:52)
Gradient Boosting Regression (4:10)
ML Model Selection (5:31)
Conclusions to Section 7 (1:45)
BONUS LECTURE: Coupon code For Applied Statistical Modelling in R
Machine Learning Regression-Tree Based Methods
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