Machine Learning With R for Data-Driven Analysis

Machine Learning With R explores how R programming supports data science workflows, from preparing datasets and selecting features to building and evaluating predictive models. The course focuses on applying R’s statistical and visualization capabilities to classification, regression, clustering, and model assessment, helping learners interpret results and make evidence-based decisions from data.

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Khushbu

Sap hcm

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Nishant Kaushik

SAP MM

Upskilled

manideepak

Manager

Career switch

Hari Shankar Jha

Senior System Admin

Upskilled

alok kumar singh

sap hcm

Placed

Jeevak

Consultant

Upskilled

Abhijit S. Getme

Consultant Engineer

Placed

Kartik Singh

SAP SD

Upskilled

Hemant Khainar

SAP MM Consultant

Placed

Neeraj Khanna

SAP MM

Placed

Burna Vinay Kumar

SAP SD Consultant

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Shubham

SAP ABAP Consultant

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Shashikant

Structure Engineer

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Khushbu

Sap hcm

Curriculum

Machine Learning With R, ZeblearnIndia, online course, data science, R programming Course Syllabus Structure

Machine learning with R equips you to preprocess massive datasets efficiently. You will master data cleaning using dplyr and feature engineering with recipes in a live notebook environment.

Getting Started

R Environment

R Packages

R Data Types: Vectors

R Data Frames

List

Factor and Matrix

R History and Scripts

R Functions

Errors

Introduction to Data Handling

Importing the Datasets

Checklist

Subsetting the Data

Subsetting Variable Condition

Calculated Fields: ifelse

Sorting and Duplicates

Joining and Merging

Exporting the Data

Introduction and Sampling

Descriptive Statistics

Percentiles and Quartiles

Box Plots

Creating Graphs and Conclusions

Introduction to Data Cleaning and Model Building Cycle

Model Building Cycle

Data Cleaning Case Study

CS Lab Step One: Basic Content of Dataset

Variable Level Exploration: Categorical

Reading Data Dictionary

Step Two Lab: Categorical Variable Exploration

Step Three Lab: Variable Level Exploration Continues

Data Cleaning and Treatment

Introduction and Correlation

LBA Correlation Calculation in R

Beyond Pearson Correlation

From Correlation to Regression

Regression Line Fitting in R

R Squared

Multiple Regression

Adjusted R Squared

Issue with Multiple Regression

Multicollinearity

Regression Conclusion

Need of Non-Linear Regression

Logistic Function and Line

Multiple Logistic Regression

Goodness of Fit for a Logistic Regression

Multicollinearity in Logistic Regression

Individual Impact of Variables

Model Selection

Logistic Regression Conclusion

Introduction to Decision Tree and Segmentation

The Decision Tree Philosophy & The Decision Tree Approach

The Splitting Criterion & Entropy Calculation

Information Gain & Calculation

The Decision Tree Algorithm

Split for Variable & The Decision Tree Lab - Part 1

The Decision Tree Lab - Part 2 & Validation

The Decision Tree Lab - Part 3 & Overfitting

Pruning & Complexity Parameters

Choosing Cp & Cross Validation Error

Two Types of Pruning

Tree Building and Model Selection

Conclusion

Introduction to Model Selection

Sensitivity Specificity

Sensitivity Specificity Continued

ROC AUC

The Best Model

Errors

Overfitting Underfitting

Bias_Variance Tradeoff

Holdout Data Validation

Ten Fold CV

Kfold CV

MSCV Conclusion

Introduction and Logistic Regression Recap

Decision Boundary

Non-Linear Decision Boundary NN

Non-Linear Decision Boundary and Solution

Neural Net Intuition

Neural Net Algorithm

Neural Net Algorithm Demo

Building a Neural Network

Local Vs Global Min

Digit Recognizer Second Attempt Part 1

Digit Recognizer Second Attempt Part 2

Lab Digit Recognizer

Conclusion

Introduction to SVM

The Classifier and Decision Boundary

SVM - The Large Margin Classifier

The SVM Algorithm and Results

SVM on R

Non-Linear Boundary

Kernel Trick

Kernel Trick on R

Soft Margin and Validation

SVM Advantage, Disadvantage, and Applications

Lab Digit Recognizer

SVM Conclusion

Introduction to Bagging, RF, and Boosting

Wisdom of the Crowd

Ensemble Learning

Ensemble Models

Bagging

Bagging Models

Random Forest

Random Forest Lab

Boosting

Boosting Illustration

Boosting Lab

Conclusion

Introduction to Clustering via Segmentation

Similarities and Dissimilarities

Calculating the Distance

Clustering Algorithms - K-means

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Machine learning with R unlocks access to premium, hands‑on labs. Enroll now to access exclusive labs featuring real‑world datasets from finance, healthcare, and e‑commerce sectors.

Evaluation & Practice Sessions

Participate in expert-led mock interviews and skill evaluation sessions after program completion.

Learning Simulation (LMS)

Lifetime access to LMS with recorded sessions, hands-on practice, and updated learning resources.

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SOLD OUT	30 Aug 2026	Weekend	SAT - SUN (08 Weeks)	18:00 - 20:00
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AVAILABLE	9 Sep 2026	Weekend	SAT - SUN (08 Weeks)	10:00 - 12:00

FAQ

Frequently Asked Questions

Q: What prerequisites are needed for a Machine Learning With R online course? A: A basic understanding of statistics and familiarity with R syntax are sufficient to start Machine Learning With R at Zeblearn. Q: How does Zeblearn's Machine Learning With R course apply data science concepts? A: The course integrates real-world data sets to illustrate preprocessing, model training, and evaluation using R's tidyverse and caret packages. Q: What career roles can be pursued after completing Machine Learning With R training? A: Graduates can qualify for data analyst, machine learning engineer, or R‑focused data scientist positions.

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Machine Learning With R for Data-Driven Analysis

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Machine Learning With R, ZeblearnIndia, online course, data science, R programming Course Syllabus Structure

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