Book description
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.
About This Book
 Build your confidence with R and find out how to solve a huge range of datarelated problems
 Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today
 Don't just learn ? apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis
Who This Book Is For
Aimed for intermediatetoadvanced people (especially data scientist) who are already into the field of data science
What You Will Learn
 Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results
 Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action
 Solve interesting realworld problems using machine learning and R as the journey unfolds
 Write reusable code and build complete machine learning systems from the ground up
 Learn specialized machine learning techniques for text mining, social network data, big data, and more
 Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems
 Evaluate and improve the performance of machine learning models
 Learn specialized machine learning techniques for text mining, social network data, big data, and more
In Detail
R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it?
This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.
In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.
For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media.
This Learning Path has been curated from three Packt products:
 R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar
 Machine Learning with R Learning  Second Edition By Brett Lantz
 Mastering Machine Learning with R By Cory Lesmeister
Style and approach
This is an enticing learning path 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 realworld problem involving handson work thus giving you a deep insight into the world of machine learning.
Publisher resources
Table of contents

R: Unleash Machine Learning Techniques
 Table of Contents
 R: Unleash Machine Learning Techniques
 R: Unleash Machine Learning Techniques
 Credits
 Preface

I. Module 1
 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

II. Module 2
 1. Introducing Machine Learning

2. Managing and Understanding Data
 R data structures
 Managing data with R

Exploring and understanding data
 Exploring the structure of data

Exploring numeric variables
 Measuring the central tendency – mean and median
 Measuring spread – quartiles and the fivenumber summary
 Visualizing numeric variables – boxplots
 Visualizing numeric variables – histograms
 Understanding numeric data – uniform and normal distributions
 Measuring spread – variance and standard deviation
 Exploring categorical variables
 Exploring relationships between variables
 Summary
 3. Lazy Learning – Classification Using Nearest Neighbors
 4. Probabilistic Learning – Classification Using Naive Bayes

5. Divide and Conquer – Classification Using Decision Trees and Rules
 Understanding decision trees
 Example – identifying risky bank loans using C5.0 decision trees
 Understanding classification rules
 Example – identifying poisonous mushrooms with rule learners
 Summary

6. Forecasting Numeric Data – Regression Methods
 Understanding regression
 Example – predicting medical expenses using linear regression
 Understanding regression trees and model trees
 Example – estimating the quality of wines with regression trees and model trees
 Summary
 7. Black Box Methods – Neural Networks and Support Vector Machines
 8. Finding Patterns – Market Basket Analysis Using Association Rules
 9. Finding Groups of Data – Clustering with kmeans
 10. Evaluating Model Performance
 11. Improving Model Performance
 12. Specialized Machine Learning Topics

III. Module 3
 1. A Process for Success
 2. Linear Regression – The Blocking and Tackling of Machine Learning
 3. Logistic Regression and Discriminant Analysis
 4. Advanced Feature Selection in Linear Models
 5. More Classification Techniques – KNearest Neighbors and Support Vector Machines
 6. Classification and Regression Trees
 7. Neural Networks
 8. Cluster Analysis
 9. Principal Components Analysis

10. Market Basket Analysis and Recommendation Engines
 An overview of a market basket analysis
 Business understanding
 Data understanding and preparation
 Modeling and evaluation
 An overview of a recommendation engine
 Business understanding and recommendations
 Data understanding, preparation, and recommendations
 Modeling, evaluation, and recommendations
 Summary
 11. Time Series and Causality
 12. Text Mining
 A. R Fundamentals
 A. Bibliography
 Index
Product information
 Title: R: Unleash Machine Learning Techniques
 Author(s):
 Release date: October 2016
 Publisher(s): Packt Publishing
 ISBN: 9781787127340
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