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1. Introduction

   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

   Troubleshooting

   Summary and Conclusion