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Course Details

 Course Code: ML0151EN

 Audience: Anyone interested in Data Science

 Course Level: Intermediate

 Time to Complete: 12 hours

 Learning Path: Applied Data Science with R

About the Course

This course dives into the basic of machine learning using R, an approachable and well-known programming language. You will learn about supervised and unsupervised learning, analyze the relation between statistical modelling and machine learning through comparisons.

You will also look into real-life examples to look at the various ways that machine learning affects the society. You will also explore many algorithms and models –

  • Popular algorithms: Classification, Regression, Clustering, Dimensional Reduction
  • Popular models: Train/Test Split, Root Mean Squared Error, Random Forests

Course Syllabus

  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters - Single Linkage Clustering
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges


  • R programming

Recommended Existing Skills

  • JupyterLab. You can enrol for our free course for JupyterLab here.
  • Data Analysis with R. You can take the course here.

Course Staff


Saeed Aghabozorgi, PhD.

Saeed Aghabozorgi, PhD. He is a Sr. Data Scientist at IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge. He is a data mining field researcher and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

Other Contributors

Daniel Tran, Kevin Wong also contributed.