R Machine Learning By Example
eBook - ePub

R Machine Learning By Example

Raghav Bali, Dipanjan Sarkar

Share book
  1. 340 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

R Machine Learning By Example

Raghav Bali, Dipanjan Sarkar

Book details
Book preview
Table of contents
Citations

About This Book

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

About This Book

  • Get to grips with the concepts of machine learning through exciting real-world examples
  • Visualize and solve complex problems by using power-packed R constructs and its robust packages for machine learning
  • Learn to build your own machine learning system with this example-based practical guide

Who This Book Is For

If you are interested in mining useful information from data using state-of-the-art techniques to make data-driven decisions, this is a go-to 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 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

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 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.

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 real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.

Frequently asked questions

How do I cancel my subscription?
Simply head over to the account section in settings and click on “Cancel Subscription” - it’s as simple as that. After you cancel, your membership will stay active for the remainder of the time you’ve paid for. Learn more here.
Can/how do I download books?
At the moment all of our mobile-responsive ePub books are available to download via the app. Most of our PDFs are also available to download and we're working on making the final remaining ones downloadable now. Learn more here.
What is the difference between the pricing plans?
Both plans give you full access to the library and all of Perlego’s features. The only differences are the price and subscription period: With the annual plan you’ll save around 30% compared to 12 months on the monthly plan.
What is Perlego?
We are an online textbook subscription service, where you can get access to an entire online library for less than the price of a single book per month. With over 1 million books across 1000+ topics, we’ve got you covered! Learn more here.
Do you support text-to-speech?
Look out for the read-aloud symbol on your next book to see if you can listen to it. The read-aloud tool reads text aloud for you, highlighting the text as it is being read. You can pause it, speed it up and slow it down. Learn more here.
Is R Machine Learning By Example an online PDF/ePUB?
Yes, you can access R Machine Learning By Example by Raghav Bali, Dipanjan Sarkar in PDF and/or ePUB format, as well as other popular books in Informatique & Modélisation et conception de données. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Packt Publishing
Year
2016
ISBN
9781784390846
Edition
1
Topic
Informatique
Subtopic
Modélisation et conception de données

R Machine Learning By Example

Table of Contents

R Machine Learning By Example
Credits
About the Authors
About the Reviewer
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Getting Started with R and Machine Learning
Delving into the basics of R
Using R as a scientific calculator
Operating on vectors
Special values
Data structures in R
Vectors
Creating vectors
Indexing and naming vectors
Arrays and matrices
Creating arrays and matrices
Names and dimensions
Matrix operations
Lists
Creating and indexing lists
Combining and converting lists
Data frames
Creating data frames
Operating on data frames
Working with functions
Built-in functions
User-defined functions
Passing functions as arguments
Controlling code flow
Working with if, if-else, and ifelse
Working with switch
Loops
Advanced constructs
lapply and sapply
apply
tapply
mapply
Next steps with R
Getting help
Handling packages
Machine learning basics
Machine learning – what does it really mean?
Machine learning – how is it used in the world?
Types of machine learning algorithms
Supervised machine learning algorithms
Unsupervised machine learning algorithms
Popular machine learning packages in R
Summary
2. Let's Help Machines Learn
Understanding machine learning
Algorithms in machine learning
Perceptron
Families of algorithms
Supervised learning algorithms
Linear regression
K-Nearest Neighbors (KNN)
Collecting and exploring data
Normalizing data
Creating training and test data sets
Learning from data/training the model
Evaluating the model
Unsupervised learning algorithms
Apriori algorithm
K-Means
Summary
3. Predicting Customer Shopping Trends with Market Basket Analysis
Detecting and predicting trends
Market basket analysis
What does market basket analysis actually mean?
Core concepts and definitions
Techniques used for analysis
Making data driven decisions
Evaluating a product contingency matrix
Getting the data
Analyzing and visualizing the data
Global recommendations
Advanced contingency matrices
Frequent itemset generation
Getting started
Data retrieval and transformation
Building an itemset association matrix
Creating a frequent itemsets generation workflow
Detecting shopping trends
Association rule mining
Loading dependencies and data
Exploratory analysis
Detecting and predicting shopping trends
Visualizing association rules
Summary
4. Building a Product Recommendation System
Understanding recommendation systems
Issues with recommendation systems
Collaborative filters
Core concepts and definitions
The collaborative filtering algorithm
Predictions
Recommendations
Similarity
Building a recommender engine
Matrix factorization
Implementation
Result interpretation
Production ready recommender engines
Extract, transform, and analyze
Model preparation and prediction
Model evaluation
Summary
5. Credit Risk Detection and Prediction – Descriptive Analytics
Types of analytics
Our next challenge
What is credit risk?
Getting the data
Data preprocessing
Dealing with missing values
Datatype conversions
Data analysis and transformation
Building analysis utilities
Analyzing the dataset
Saving the transformed dataset
Next steps
Feature sets
Machine learning algorithms
Summary
6. Credit Risk Detection and Prediction – Predictive Analytics
Predictive analytics
How to predict credit risk
Important concepts in predictive modeling
Preparing the data
Building predictive models
Evaluating predictive models
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
Social networks (Twitter)
Data mining @social networks
Mining social network data
Data and visualization
Word clouds
Treemaps
Pixel-oriented maps
Other visualizations
Getting started with Twitter APIs
Overview
Registering the application
Connect/authenticate
Extracting sample tweets
Twitter data mining
Frequent words and associations
Popular devices
Hierarchical clustering
Topic modeling
Challenges with social network data mining
References
Summary
8. Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Key concepts of sentiment analysis
Subjectivity
Sentiment polarity
Opinion summarization
Feature extraction
Approaches
Applications
Challenges
Sentiment analysis upon Tweets
Polarity analysis
Classification-based algorithms
Labeled dataset
Support Vector Machines
Ensemble methods
Boosting
Cross-validation
Summary
Index

R Machine Learning By Example

Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has...

Table of contents