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Machine Learning with R CognitiveClass

Enrollment in this course is by invitation only
This Machine Learning with R course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
  • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
  • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!

Course Syllabus

Module 1 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
  • Machine Learning Languages, Types, and Examples 
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning 
  • Supervised Learning Classification 
  • Unsupervised Learning 
Module 2 - Supervised Learning I
  • K-Nearest Neighbors 
  • Decision Trees 
  • Random Forests
  • Reliability of Random Forests 
  • Advantages & Disadvantages of Decision Trees 
 Module 3 - Supervised Learning II
  • Regression Algorithms 
  • Model Evaluation 
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models 
 Module 4 - Unsupervised Learning
  • 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 
Module 5 - Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection 
  • Collaborative Filtering & Its Challenges 


  • Understanding of the R programming language
Dr. Saeed Aghabozorgi, TensorFlow Course Instructor
Saeed Aghabozorgi, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.
Polong Lin, Data Scientist
Polong Lin is a Data Scientist at IBM in Canada. Under the Emerging Technologies division, Polong is responsible for educating the next generation of data scientists through BDU. Polong is a regular speaker in conferences and meetups, and holds a M.Sc. in Cognitive Psychology.
Course Staff
  1. Course Number

  2. Classes Start

    Any Time, Self-Paced
  3. Estimated Effort

    3 hours