R Machine Learning By Example

Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully

R Machine Learning By Example

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Raghav Bali, Dipanjan Sarkar

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Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully
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Book Details

ISBN 139781784390846
Paperback340 pages

Book Description

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 data-driven 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 real-world data problems.

This book takes you on a data-driven journey that starts with the very basics of R and machine learning and gradually builds upon the concepts to work on projects that tackle real-world 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 e-commerce, finance, and social-media, which are at the very core of this data-driven 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 data-related problems and solve them using the powerful yet simple language, R.

Table of Contents

Chapter 1: Getting Started with R and Machine Learning
Delving into the basics of R
Data structures in R
Working with functions
Controlling code flow
Advanced constructs
Next steps with R
Machine learning basics
Summary
Chapter 2: Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Families of algorithms
Summary
Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
Evaluating a product contingency matrix
Frequent itemset generation
Association rule mining
Summary
Chapter 4: Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Building a recommender engine
Production ready recommender engines
Summary
Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Data analysis and transformation
Next steps
Summary
Chapter 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
Chapter 7: Social Media Analysis – Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Getting started with Twitter APIs
Twitter data mining
Challenges with social network data mining
References
Summary
Chapter 8: Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment analysis upon Tweets
Summary

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 real-world 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 real-world problems in machine learning and data science

Authors

Table of Contents

Chapter 1: Getting Started with R and Machine Learning
Delving into the basics of R
Data structures in R
Working with functions
Controlling code flow
Advanced constructs
Next steps with R
Machine learning basics
Summary
Chapter 2: Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Families of algorithms
Summary
Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
Evaluating a product contingency matrix
Frequent itemset generation
Association rule mining
Summary
Chapter 4: Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Building a recommender engine
Production ready recommender engines
Summary
Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Data analysis and transformation
Next steps
Summary
Chapter 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
Chapter 7: Social Media Analysis – Analyzing Twitter Data
Social networks (Twitter)
Data mining @social networks
Getting started with Twitter APIs
Twitter data mining
Challenges with social network data mining
References
Summary
Chapter 8: Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment analysis upon Tweets
Summary

Book Details

ISBN 139781784390846
Paperback340 pages
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