by Antoine Pissoort | Released April 2018 | ISBN: 9781788622820

This course will give you the required knowledge and skills to build real-world machine learning projects with R.About This VideoEffectively explore and prepare data in R and RStudioTrain, 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.In DetailUnsupervised 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. Show and hide more

1. Chapter 1 : Machine Learning Model in R
   • The Course Overview 00:03:30
   • The Benefits of Deploying Machine Learning Models 00:12:36
   • R for Machine Learning 00:08:57
   • Choosing a Machine Learning Algorithm 00:08:46
   • Data Exploration – Online Retail Dataset Sample 00:09:31
2. Chapter 2 : Exploring K-Means Clustering
   • K-Means Clustering Model 00:09:48
   • Data Preparation Using Online Retail Dataset 00:13:11
   • Model Diagnostics – How Do I Find K? 00:11:35
   • Training Your Model 00:11:00
   • Evaluating and Improving Your Model 00:10:30
3. Chapter 3 : Principal Component Analysis (PCA)
   • What Is Principal Component Analysis? 00:12:47
   • Implementing and Visualizing PCA Features 00:14:13
   • Implementing and Visualizing PCA Individuals 00:06:31
   • Evaluate Your PCA 00:16:08
4. Chapter 4 : Pattern Mining
   • Market Basket Analysis for Transactional Data 00:12:20
   • Computing Item Sets – Association Rules 00:14:37
   • Visualizing Item Sets 00:12:52
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