R: Unleash Machine Learning Techniques

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.

R: Unleash Machine Learning Techniques

Raghav Bali et al.

1 customer reviews
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.
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Book Details

ISBN 139781787127340
Paperback1123 pages

Book Description

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:

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
Chapter 9: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 10: Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 11: Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 12: Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – performing OCR with SVMs
Summary
Chapter 16: Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Chapter 17: Finding Groups of Data – Clustering with k-means
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary
Chapter 18: Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 19: Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 20: Specialized Machine Learning Topics
Working with proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
Chapter 21: A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 22: Linear Regression – The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 23: Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Model selection
Summary
Chapter 24: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Summary
Chapter 25: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Feature selection for SVMs
Summary
Chapter 26: Classification and Regression Trees
Introduction
An overview of the techniques
Business case
Summary
Chapter 27: Neural Networks
Neural network
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 28: Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 29: Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 30: 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
Chapter 31: Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 32: Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary

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 real-world 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

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
Chapter 9: Introducing Machine Learning
The origins of machine learning
Uses and abuses of machine learning
How machines learn
Machine learning in practice
Machine learning with R
Summary
Chapter 10: Managing and Understanding Data
R data structures
Managing data with R
Exploring and understanding data
Summary
Chapter 11: Lazy Learning – Classification Using Nearest Neighbors
Understanding nearest neighbor classification
Example – diagnosing breast cancer with the k-NN algorithm
Summary
Chapter 12: Probabilistic Learning – Classification Using Naive Bayes
Understanding Naive Bayes
Example – filtering mobile phone spam with the Naive Bayes algorithm
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Black Box Methods – Neural Networks and Support Vector Machines
Understanding neural networks
Example – Modeling the strength of concrete with ANNs
Understanding Support Vector Machines
Example – performing OCR with SVMs
Summary
Chapter 16: Finding Patterns – Market Basket Analysis Using Association Rules
Understanding association rules
Example – identifying frequently purchased groceries with association rules
Summary
Chapter 17: Finding Groups of Data – Clustering with k-means
Understanding clustering
Example – finding teen market segments using k-means clustering
Summary
Chapter 18: Evaluating Model Performance
Measuring performance for classification
Estimating future performance
Summary
Chapter 19: Improving Model Performance
Tuning stock models for better performance
Improving model performance with meta-learning
Summary
Chapter 20: Specialized Machine Learning Topics
Working with proprietary files and databases
Working with online data and services
Working with domain-specific data
Improving the performance of R
Summary
Chapter 21: A Process for Success
The process
Business understanding
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Chapter 22: Linear Regression – The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Other linear model considerations
Summary
Chapter 23: Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
Model selection
Summary
Chapter 24: Advanced Feature Selection in Linear Models
Regularization in a nutshell
Business case
Modeling and evaluation
Model selection
Summary
Chapter 25: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
K-Nearest Neighbors
Support Vector Machines
Business case
Feature selection for SVMs
Summary
Chapter 26: Classification and Regression Trees
Introduction
An overview of the techniques
Business case
Summary
Chapter 27: Neural Networks
Neural network
Deep learning, a not-so-deep overview
Business understanding
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Summary
Chapter 28: Cluster Analysis
Hierarchical clustering
K-means clustering
Gower and partitioning around medoids
Data understanding and preparation
Modeling and evaluation
Summary
Chapter 29: Principal Components Analysis
An overview of the principal components
Modeling and evaluation
Summary
Chapter 30: 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
Chapter 31: Time Series and Causality
Univariate time series analysis
Modeling and evaluation
Summary
Chapter 32: Text Mining
Text mining framework and methods
Topic models
Modeling and evaluation
Summary

Book Details

ISBN 139781787127340
Paperback1123 pages
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From 1 reviews

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