Course Outline


Setting up the R Development Environment

Deep Learning vs Neural Network vs Machine Learning

Building an Unsupervised Learning Model

Case Study: Predicting an Outcome Using Existing Data

Preparing Test and Training Data Sets For Analysis

Clustering Data

Classifying Data

Visualizing Data

Evaluating the Performance of a Model

Iterating Through Model Parameters

Hyper-parameter Tuning 

Integrating a Model with a Real-World Application

Deploying a Machine Learning Application


Summary and Conclusion


  • R programming experience
  • An understanding of machine learning concepts
  21 Hours


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