Cognitive Class

Machine Learning with R

Learn what machine learning is all about in this beginner-friendly course. Through videos and labs, learn how to apply different machine learning techniques such as classification, clustering, neural networks, regression, and recommender systems.

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About This Machine Learning with R Course

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 

Requirements

Recommended skills prior to taking this course

  • You have to do hands-on lab for this course. The tool that you use for hands-on is called Jupyter and it is one of the most popular tools used by data scientists. If you are not familiar with Jupyter, I would recommend that you take our free Data Science Hands-on with Open Source Tools.
  • This hands-on lab requires that you have working knowledge of R programming language as it applies to data analytics. If you don't feel you have sufficient skill in Data Analysis with R, I recommend you take Data Analysis with R courses.

Course Staff

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.

Cognitive Class Course Development Team

Thanks to the course contributors: Daniel Tran, Kevin Wong