Book description
Learn how to use R to apply powerful machine learning methods and gain insight into realworld applications using clustering, logistic regressions, random forests, support vector machine, and more.
Key Features
 Use R 3.5 to implement realworld examples in machine learning
 Implement key machine learning algorithms to understand the working mechanism of smart models
 Create endtoend machine learning pipelines using modern libraries from the R ecosystem
Book Description
Machine Learning with R Quick Start Guide takes you on a datadriven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline.
From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling.
By the end of the book, you will be able to apply the concepts of machine learning, deal with datarelated problems, and solve them using the powerful yet simple language that is R.
What you will learn
 Introduce yourself to the basics of machine learning with R 3.5
 Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results
 Learn to build predictive models with the help of various machine learning techniques
 Use R to visualize data spread across multiple dimensions and extract useful features
 Use interactive data analysis with R to get insights into data
 Implement supervised and unsupervised learning, and NLP using R libraries
Who this book is for
This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
Publisher resources
Table of contents
 Title Page
 Copyright and Credits
 About Packt
 Contributors
 Preface

R Fundamentals for Machine Learning
 R and RStudio installation
 Some basic commands
 Objects, special cases, and basic operators in R
 Controlling code flow
 All about R packages
 Taking further steps
 Summary
 Predicting Failures of Banks  Data Collection
 Predicting Failures of Banks  Descriptive Analysis
 Predicting Failures of Banks  Univariate Analysis
 Predicting Failures of Banks  Multivariate Analysis
 Visualizing Economic Problems in the European Union
 Sovereign Crisis  NLP and Topic Modeling
 Other Books You May Enjoy
Product information
 Title: Machine Learning with R Quick Start Guide
 Author(s):
 Release date: March 2019
 Publisher(s): Packt Publishing
 ISBN: 9781838644338
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