Advanced Machine Learning with R

• R programming experience
• An understanding of machine learning concepts

In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.

By the end of this training, participants will be able to:

• Understand and implement unsupervised learning techniques
• Apply clustering and classification to make predictions based on real world data.
• Visualize data to quicly gain insights, make decisions and further refine analysis.
• Improve the performance of a machine learning model using hyper-parameter tuning.
• Put a model into production for use in a larger application.
• Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.

Audience

• Developers
• Analysts
• Data scientists

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

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