This course will give you the required knowledge and skills to build real-world machine learning projects with R.
About This Video
- 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.
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.
Table of Contents
- Chapter 1 : Machine Learning Model in R
- Chapter 2 : Exploring K-Means Clustering
- Chapter 3 : Principal Component Analysis (PCA)
- Chapter 4 : Pattern Mining
- Title: Unsupervised Machine Learning Projects with R
- Release date: April 2018
- Publisher(s): Packt Publishing
- ISBN: 9781788622820