# The Complete R Programming Certification Bundle

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6 Courses & 35 Hours
\$29.00\$1,200.00
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## What's Included

• Certification included
• Experience level required: All levels
• Access 50 lectures & 6 hours of content 24/7
• Length of time users can access this course: Lifetime

## Course Curriculum

### 50 Lessons (6h)

• Welcome to Regression Modelling With R
Introduction to the Course6:58
Data and Code
Install R and RStudio6:36
Data Cleaning17:12
More Data Cleaning8:05
Exploratory Data Analysis (EDA)18:53
Conclusions to Section 11:58
• Ordinary Least Square Regression
Ordinary Least Square Regression: Theory10:44
OLS Implementation8:40
Confidence Interval-Theory6:06
Calculate the Confidence Interval in R4:53
Confidence Interval and OLS Regressions7:19
Linear Regression without Intercept3:40
Implement ANOVA on OLS Regression3:37
Multiple Linear Regression6:27
Multiple Linear regression with Interaction and Dummy Variables15:05
Some Basic Conditions that OLS Models Have to Fulfil12:56
Conclusions to Section 22:55
• Deal with Multicollinearity in OLS Regression Models
Identify Multicollinearity16:42
Doing Regression Analyses with Correlated Predictor Variables5:36
Principal Component Regression in R10:39
Partial Least Square Regression in R7:33
Lasso Regression in R4:24
Conclusions to Section 32:00
• Variable & Model Selection
Why Do Any Kind of Selection?4:40
Select the Most Suitable OLS Regression Model13:19
Select Model Subsets8:22
Machine Learning Perspective on Evaluate Regression Model Accuracy7:10
Evaluate Regression Model Performance14:26
LASSO Regression for Variable Selection3:42
Identify the Contribution of Predictors in Explaining the Variation in Y8:38
Conclusions to Section 41:35
• Dealing With Other Violations of the OLS Conditions
Data Transformations
Robust Regression: Deal With Outliers6:58
Deal With Heteroelasticity7:12
Conclusion to Section 51:12
• Generalised Linear Models (GLMs)
What are GLMs?5:25
Implement a Logistic Regression16:18
More Logistic Regression9:10
Modelling Count Data6:19
Multinomial Regression6:11
Conclusion to Section 62:12
• Non-Parametric and Machine Learning Regression
Polynomial Regression18:19
Generalized Additive Models (GAMs) in R14:09
Boosted GAM6:15
CART For Regression10:54
CIR5:45
Random Forest (RF) Regression11:52

### Statistics & Machine Learning for Regression Modeling with R

MS
Minerva Singh

Instructor

Minerva Singh is a Ph.D. graduate from Cambridge University where she specialized in Tropical Ecology. She is also a Data Scientist on the side. As a part of her research, she has to carry out extensive data analysis, including spatial data analysis using tools like R, QGIS, and Python. Minerva also holds an MPhil degree in Geography and Environment from Oxford University.

## Description

Regression analysis is one of the central aspects of both statistical and machine learning-based analysis. This course will teach you regression analysis for both statistical data analysis and machine learning in R in a practical hands-on manner. This course can help you achieve better grades, give you new analysis tools for your academic career, implement your knowledge in a work setting or make business forecasting related decisions.

• Access 50 lectures & 6 hours of content 24/7
• Implement & infer Ordinary Least Square (OLS) regression using R
• Build machine learning-based regression models & test their robustness in R
• Apply statistical and machine learning-based regression models to deals with problems such as multicollinearity
• Learn when & how machine learning models should be applied
Note: Software not included

## Specs

Important Details

• Length of time users can access this course: lifetime
• Access options: web & mobile streaming
• Certification of completion included