This is the code repository for R Machine Learning Projects, published by Packt.
Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.
This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.
By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.
This book covers the following exciting features:
- Explore deep neural networks and various frameworks that can be used in R
- Develop a joke recommendation engine to recommend jokes that match users’ tastes
- Create powerful ML models with ensembles to predict employee attrition
- Build autoencoders for credit card fraud detection
- Work with image recognition and convolutional neural networks
- Make predictions for casino slot machine using reinforcement learning
- Implement NLP techniques for sentiment analysis and customer segmentation
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
setwd("~/Desktop/chapter 2")
library(rsample)
data(attrition)
str(attrition)
mydata<-attrition
Following is what you need for this book: 0
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 2 to 10 | RStudio, R3.5 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Machine Learning with R Cookbook - Second Edition [Packt] [Amazon]
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Machine Learning Algorithms - Second Edition [Packt] [Amazon]
Dr. Sunil Kumar Chinnamgari has a PhD in computer science (specializing in machine learning and natural language processing). He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of a lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meet-ups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.
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