Advanced Machine Learning with R Training Course

  1. Artificial Intelligence (AI) Training
  2. Machine Learning Training
  3. Deep Learning Training
  4. Advanced Machine Learning with R Training Course

Course Code

radvml

Duration

21 hours (usually 3 days including breaks)

Requirements

  • R programming experience
  • An understanding of machine learning concepts

Overview

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

Course Outline

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

Related Categories

Related Courses

Programming with Big Data in RR FundamentalsData Analytics With RData Mining with RForecasting with RMachine Learning Fundamentals with RMarketing Analytics using RR for Data Analysis and Research Introduction to RIntroductory R for BiologistsRNeural Network in RAdvanced R ProgrammingR Programming for Data AnalysisBuilding Web Applications in R with Shiny

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