In this final chapter, we went through the process of quickly and simply getting R and RStudio running on the cloud. Utilizing AWS in this exercise, we covered step by step how to create a virtual machine (an instance) on the cloud, configure it, launch it and bring up RStudio on a web browser. Finally, we went over how easy it is to load data, by bringing in the climate .csv
file from GitHub. With this introduction to cloud computing, you can now perform work anywhere you have an Internet connection, and can quickly scale the power of your instance to meet your needs. That concludes the primary chapters of the book. I hope you've enjoyed it and can implement the methods in here as well as other methods you learn over time. Thank You!
Mastering Machine Learning with R, Second Edition - Second Edition
Mastering Machine Learning with R, Second Edition - Second Edition
Overview of this book
This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more.
You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you’ll understand how these concepts work and what they do.
With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets.
Table of Contents (23 chapters)
Title Page
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
Free Chapter
A Process for Success
Linear Regression - The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks and Deep Learning
Cluster Analysis
Principal Components Analysis
Market Basket Analysis, Recommendation Engines, and Sequential Analysis
Creating Ensembles and Multiclass Classification
Time Series and Causality
Text Mining
R on the Cloud
R Fundamentals
Sources
Customer Reviews