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Machine Learning with R

No more textbook confusion. Learn applied machine learning with R and finally connect the dots between data, models, and real results.

(ML-R.AE1) / ISBN : 978-1-64459-694-4
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About This Course

Most people know R but freeze when it’s time to apply it to machine learning. That’s where our Machine Learning with R training changes the game. This isn’t just another tutorial or theory dump. You will gain practical knowledge and real world datasets to build predictive models and solve problems that actually exist. Consider this as a mirror world learning of what today’s data scientists face in their day-to-day routine.  By the end, you won’t just know machine learning, you’ll be ready to use it. Whether you're aiming to land a role in data science, add ML projects to your portfolio, or just get better at turning data into decisions, this machine learning with R training gives you the confidence and skills to stand out. Perfect for anyone serious about data science with R and machine learning.And tired of courses that overpromise and underdeliver.

Skills You’ll Get

  • Real-World Machine Learning Skills: Learn how to build, test, and tune machine learning models using real datasets.
  • Mastery of R for Machine Learning: Go beyond the basics and actually apply R programming to solve real-world problems.
  • A Complete ML Workflow, Step-by-Step: From data prep to prediction, you’ll understand how machine learning works in the real world.
  • Portfolio-Worthy Projects: Build hands-on projects that prove you can turn raw data into smart decisions. Great for resumes, interviews, and LinkedIn.
  • Confidence to Solve Real Problems: Whether it’s churn prediction, classification, or clustering, you’ll know how to choose the right model and make it work.
  • Job-Ready Knowledge: You won’t just learn machine learning, you’ll think like a data scientist and have the skills to back it up.
Download Course Outline

1

Introduction

  • What Does This Course Cover?
2

What Is Machine Learning?

  • Discovering Knowledge In Data
  • Machine Learning Techniques
  • Model Selection
  • Model Evaluation
3

Introduction to R and RStudio

  • Welcome To R
  • R And RStudio Components
  • Writing And Running An R Script
  • Data Types In R
4

Managing Data

  • The tidyverse
  • Data Collection
  • Data Exploration
  • Data Preparation
5

Linear Regression

  • Bicycle Rentals And Regression
  • Relationships Between Variables
  • Simple Linear Regression
  • Multiple Linear Regression
  • Case Study: Predicting Blood Pressure
6

Logistic Regression

  • Prospecting For Potential Donors
  • Classification
  • Logistic Regression
  • Case Study: Income Prediction
7

k-Nearest Neighbors

  • Detecting Heart Disease
  • k-Nearest Neighbors
  • Case Study: Revisiting The Donor Dataset
8

Naïve Bayes

  • Classifying Spam Email
  • NAÏVE Bayes
  • Case Study: Revisiting The Heart Disease Detection Problem
9

Decision Trees

  • Predicting Build Permit Decisions
  • Decision Trees
  • Case Study: Revisiting The Income Prediction Problem
10

Evaluating Performance

  • Estimating Future Performance
  • Beyond Predictive Accuracy
  • Visualizing Model Performance
11

Improving Performance

  • Parameter Tuning
  • Ensemble Methods
12

Discovering Patterns with Association Rules

  • Market Basket Analysis
  • Association Rules
  • Discovering Association Rules
  • Case Study: Identifying Grocery Purchase Patterns
13

Grouping Data with Clustering

  • Clustering
  • k-Means Clustering
  • Segmenting Colleges With -Means Clustering
  • Case Study: Segmenting Shopping Mall Customers

Any questions?
Check out the FAQs

  Read answers to commonly asked questions about this certification exam. 

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Python is more common, but R shines in data analysis and stats-heavy ML tasks. It’s loaded with ML libraries and perfect for building models fast.

R is built for data analysis. It has powerful libraries for statistics and machine learning, making it a go-to for data scientists. With libraries like caret and mlr, R is very useful when effective visualizing is required.

Yes. R is widely used in industries like finance, healthcare, and academia where data-driven decisions matter most.

You’ll work on practical, resume-worthy projects like classification, regression, clustering, and recommendation systems. These projects are based on real-world scenarios to help you build skills that employers actually look for.

Yes, absolutely. This course is designed for learners with a basic understanding of R. You don’t need to be an expert; just someone comfortable with R syntax and ready to get hands-on. Everything else, from data preprocessing to building ML models, is explained clearly and practically.

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