If you are just getting started with R, RStudio, and Machine Learning, you may already know that a great and easy way to get motivated is by looking at concrete examples and workflows from experienced people in the field. For this post, we have gathered a set of educational videos with hands-on examples on how to implement some of the most popular Machine Learning algorithms. We begin by giving you a general introduction and motivation to Machine Learning and the R language. You will be able to go along with examples and presentations, and you will get familiarized with the syntax and the procedures of implementing Machine Learning projects in R. Please enjoy watching this series of videos and try them out yourself, take advantage of this great summary and compilation to do your own experiments and models.
These videos are ranked by the number of views, and they are a set of resources for some well-known techniques and concepts from Machine Learning. It is important to keep in mind that many of the listed videos are part of different YouTube playlists and courses developed by experienced instructors and organization so don't stop with this list. Subscribe and follow the authors of these videos to get a wider array of video resources for your work.
- How to Build a Text Mining, Machine Learning Document Classification System in R! (131k views), 26 minutes. This videos will show you how to build a machine learning document classification system from scratch in less than 30 minutes using R. You will see an example using a text mining technique to identify the speaker of unmarked presidential campaign speeches. It also addresses applications to brand management, auditing, fraud detection, electronic medical records, and more.
- Principal Component Analysis Using R (115k views), 11 minutes. This tutorial guides you through a manual principal components analysis of some simple data. Will become familiarized with the underlying principles and terminology of PCA.
- K-Nearest Neighbor Algorithm in R (60k views), 15-minute walkthrough. This video introduces the k-NN (k-nearest neighbor) model in R using the famous iris dataset. This video by is part of a tutorial series on R, Data Science, and Machine Learning.
- Introduction to Cluster Analysis with R - an example (54k views), 18 minutes. This tutorial video provides an illustration of doing cluster analysis with R. It also includes, concepts like data normalization, hierarchical clustering using dendrogram, and nonhierarchical k-means clustering among others.
- Decision Tree Classification in R (40k views), 20 minutes. This video will give you an introduction to the rpart library in R to build decision trees for classification. The video provides a brief overview of decision trees and an example of visualization and prediction using the model.
- Support Vector Machines (SVM) Overview and Demo using R (35k views), 17 minutes. This is a quick overview of Support Vector Machines (SVM) using R through a set of examples and demonstrations. First, it covers the basic concepts and ideas behind SVM and then it moves onto a practical example.
- Random Forest Overview and Demo in R (24k views), 17 minutes. This video provides a brief overview of the basic concepts and principles of Random Forests. Through an example, you get to see how to implement this algorithm yourself using the randomForest library in R.
- Neural Networks in R (19k views), 19 minutes. This video is a guide to Neural networks in R. You will see how to fit, plot, and make predictions with a neural network using R's neuralnet package.
- R Programming Language for Machine Learning (14k views), 37 minutes. This video gives you a brief introduction to R and a walkthrough example of implementing a basic model.
- Introduction to Machine Learning with R and caret (9.8k views), 1-hour 40-minute presentation. This video from the Data Science Dojo gives you a general introduction to the caret (Classification And REgression Training) package in R. The will be using caret to implement some of the most popular data science tasks involved in most projects and you will see how to incorporate caret into your personal workflow.
We hope you enjoy watching these videos and hopefully, get you inspired to build your own models and make predictions. Don't forget to watch the full lectures and playlists to enhance your tutorial library.
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