Unsupervised Machine Learning Projects with R [Video]

More Information
  • Learn the benefits of deploying Machine Learning algorithms in R
  • Explore K-means clustering techniques
  • Prepare data for imputation and model diagnostics
  • Train, evaluate, and improve your models
  • Visualize the Principal Component Analysis model in 2D
  • Learn pattern mining for transactional data
  • Learn what mocking is and how to use mocking frameworks
  • Understand the selection of design patterns

Unsupervised Machine Learning Projects with R will help you build your knowledge and skills by guiding you in building machine learning projects with a practical approach and using the latest technologies provided by the R language such as Rmarkdown, R-shiny, and more. The areas this course addresses include effectively exploring and preparing data in R and RStudio and training, evaluating, and improving a model's performance (if needed). You will feel comfortable and confident after learning unsupervised and supervised Machine Learning algorithms.

In the first of the four sections comprising this course, we start by introducing you to concepts in Machine Learning, before then moving on to discuss projects in unsupervised Machine Learning. Next, we focus on two machine learning paradigms—K-Means Clustering and Principal Component Analysis—to grasp how they work and apply them to business Customer Segmentation (Market Segmentation Analysis). We finish the section by looking at the specific design aspects of Horizon 7 and how to approach a project, before finally looking at some example scenarios that will help you plan your own environment.All the work delivered into the R code script during the videos is available through nice html reports created by Rmarkdown.

By the end of the course, you will be able to train and improve real-world projects and models using unsupervised Machine Learning techniques

The code bundle for this video course is available at: https://github.com/PacktPublishing/Unsupervised-Machine-Learning-Projects-with-R

Style and Approach

Step by step practical approach to building real-world projects using unsupervised Machine Learning with R.

  • Effectively explore and prepare data in R and RStudio
  • Train, evaluate, and improve a model's performance and visualize models in 2D view.
  • Learn the best use cases, identify problem areas and resolve them with the right data science techniques and methods for your projects.
Course Length 3 hours 8 minutes
ISBN 9781788622820
Date Of Publication 29 Apr 2018


Antoine Pissoort

Antoine Pissoort is a statistician and Machine Learning enthusiast with a lot of experience in that field through various projects. He loves to play with algorithms and write code in R to develop Machine Learning models in different areas. He is always looking for the newest technologies.