Book description
Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated realworld problems successfully
About This Book
 Get to grips with the concepts of machine learning through exciting realworld examples
 Visualize and solve complex problems by using powerpacked R constructs and its robust packages for machine learning
 Learn to build your own machine learning system with this examplebased practical guide
Who This Book Is For
If you are interested in mining useful information from data using stateoftheart techniques to make datadriven decisions, this is a goto guide for you. No prior experience with data science is required, although basic knowledge of R is highly desirable. Prior knowledge in machine learning would be helpful but is not necessary.
What You Will Learn
 Utilize the power of R to handle data extraction, manipulation, and exploration techniques
 Use R to visualize data spread across multiple dimensions and extract useful features
 Explore the underlying mathematical and logical concepts that drive machine learning algorithms
 Dive deep into the world of analytics to predict situations correctly
 Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
 Write reusable code and build complete machine learning systems from the ground up
 Solve interesting realworld problems using machine learning and R as the journey unfolds
 Harness the power of robust and optimized R packages to work on projects that solve realworld problems in machine learning and data science
In Detail
Data science and machine learning are some of the top buzzwords in the technical world today. From retail stores to Fortune 500 companies, everyone is working hard to making machine learning give them datadriven insights to grow their business. With powerful data manipulation features, machine learning packages, and an active developer community, R empowers users to build sophisticated machine learning systems to solve realworld data problems.
This book takes you on a datadriven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle realworld problems.
You'll begin by getting an understanding of the core concepts and definitions required to appreciate machine learning algorithms and concepts. Building upon the basics, you will then work on three different projects to apply the concepts of machine learning, following current trends and cover major algorithms as well as popular R packages in detail. These projects have been neatly divided into six different chapters covering the worlds of ecommerce, finance, and socialmedia, which are at the very core of this datadriven revolution. Each of the projects will help you to understand, explore, visualize, and derive insights depending upon the domain and algorithms.
Through this book, you will learn to apply the concepts of machine learning to deal with datarelated problems and solve them using the powerful yet simple language, R.
Style and approach
The book is an enticing journey that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real realworld problem involving handson work thus giving you a deep insight into the world of machine learning.
Publisher resources
Table of contents

R Machine Learning By Example
 Table of Contents
 R Machine Learning By Example
 Credits
 About the Authors
 About the Reviewer
 www.PacktPub.com
 Preface
 1. Getting Started with R and Machine Learning
 2. Let's Help Machines Learn
 3. Predicting Customer Shopping Trends with Market Basket Analysis
 4. Building a Product Recommendation System
 5. Credit Risk Detection and Prediction – Descriptive Analytics

6. Credit Risk Detection and Prediction – Predictive Analytics
 Predictive analytics
 How to predict credit risk
 Important concepts in predictive modeling
 Getting the data
 Data preprocessing
 Feature selection
 Modeling using logistic regression
 Modeling using support vector machines
 Modeling using decision trees
 Modeling using random forests
 Modeling using neural networks
 Model comparison and selection
 Summary
 7. Social Media Analysis – Analyzing Twitter Data
 8. Sentiment Analysis of Twitter Data
 Index
Product information
 Title: R Machine Learning By Example
 Author(s):
 Release date: March 2016
 Publisher(s): Packt Publishing
 ISBN: 9781784390846
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