Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5, 3rd Edition

✍ Scribed by Cory Lesmeister

• Publisher: Packt Publishing
• Year: 2019
• Tongue: English
• Leaves: 344
• Edition: 3
• Category: Library

✦ Synopsis

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications

Key Features

• Build independent machine learning (ML) systems leveraging the best features of R 3.5
• Understand and apply different machine learning techniques using real-world examples
• Use methods such as multi-class classification, regression, and clustering

Book Description

Given the growing popularity of the R-zerocost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML ,using? the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning, and reinforcement learning algorithms to design efficient and powerful ML models.

This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You'll also learn to use the latest library support, such as TensorFlow and Keras-R, for performing advanced computations. Additionally, you'll explore complex topics, such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You'll even be introduced to reinforcement learning, along with its various use cases and models. In the concluding chapters, you'll get a glimpse into how some of these blackbox models can be diagnosed and understood.

By the end of this book, you'll be equipped with the skills to deploy ML techniques in your own projects or at work.

What you will learn

• Prepare data for machine learning methods with ease
• Understand how to write production-ready code and package it for use
• Produce simple and effective data visualizations for improved insights
• Master advanced methods, such as Boosted Trees and deep neural networks
• Use natural language processing to extract insights in relation to text
• Implement tree-based classifiers, including Random Forest and Boosted Tree

Who this book is for

This book is for data science professionals, machine learning engineers, or anyone who is looking for the ideal guide to help them implement advanced machine learning algorithms. The book will help you take your skills to the next level and advance further in this field. Working knowledge of machine learning with R is mandatory.

Table of Contents

1. Preparing and Understanding Data
2. Linear Regression
3. Logistic Regression
4. Advanced Feature Selection in Linear Models
5. K-Nearest Neighbors and Support Vector Machines
6. Tree-Based Classification
7. Neural Networks and Deep Learning
8. Creating Ensembles and Multiclass Methods
9. Cluster Analysis
10. Principal Component Analysis
11. Association Analysis
12. Time Series and Causality
13. Text Mining
14. Appendix A- Creating a Package

✦ Table of Contents

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
Chapter 1: Preparing and Understanding Data
Overview
Reading the data
Handling duplicate observations
Descriptive statistics
Exploring categorical variables
Handling missing values
Zero and near-zero variance features
Treating the data
Correlation and linearity
Summary
Chapter 2: Linear Regression
Univariate linear regression
Building a univariate model
Reviewing model assumptions
Multivariate linear regression
Loading and preparing the data
Modeling and evaluation – stepwise regression
Modeling and evaluation – MARS
Reverse transformation of natural log predictions
Summary
Chapter 3: Logistic Regression
Classification methods and linear regression
Logistic regression
Model training and evaluation
Training a logistic regression algorithm
Weight of evidence and information value
Feature selection
Cross-validation and logistic regression
Multivariate adaptive regression splines
Model comparison
Summary
Chapter 4: Advanced Feature Selection in Linear Models
Regularization overview
Ridge regression
LASSO
Elastic net
Data creation
Modeling and evaluation
Ridge regression
LASSO
Elastic net
Summary
Chapter 5: K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
Manipulating data
Dataset creation
Data preparation
Modeling and evaluation
KNN modeling
Support vector machine
Summary
Chapter 6: Tree-Based Classification
An overview of the techniques
Understanding a regression tree
Classification trees
Random forest
Gradient boosting
Datasets and modeling
Classification tree
Random forest
Extreme gradient boosting – classification
Feature selection with random forests
Summary
Chapter 7: Neural Networks and Deep Learning
Introduction to neural networks
Deep learning – a not-so-deep overview
Deep learning resources and advanced methods
Creating a simple neural network
Data understanding and preparation
Modeling and evaluation
An example of deep learning
Keras and TensorFlow background
Loading the data
Creating the model function
Model training
Summary
Chapter 8: Creating Ensembles and Multiclass Methods
Ensembles
Data understanding
Modeling and evaluation
Random forest model
Creating an ensemble
Summary
Chapter 9: Cluster Analysis
Hierarchical clustering
Distance calculations
K-means clustering
Gower and PAM
Gower
PAM
Random forest
Dataset background
Data understanding and preparation
Modeling 
Hierarchical clustering
K-means clustering
Gower and PAM
Random forest and PAM
Summary
Chapter 10: Principal Component Analysis
An overview of the principal components
Rotation
Data
Data loading and review
Training and testing datasets
PCA modeling
Component extraction
Orthogonal rotation and interpretation
Creating scores from the components
Regression with MARS
Test data evaluation
Summary
Chapter 11: Association Analysis
An overview of association analysis
Creating transactional data
Data understanding
Data preparation
Modeling and evaluation
Summary
Chapter 12: Time Series and Causality
Univariate time series analysis
Understanding Granger causality
Time series data
Data exploration
Modeling and evaluation
Univariate time series forecasting
Examining the causality
Linear regression
Vector autoregression
Summary
Chapter 13: Text Mining
Text mining framework and methods
Topic models
Other quantitative analysis
Data overview
Data frame creation
Word frequency
Word frequency in all addresses
Lincoln's word frequency
Sentiment analysis
N-grams
Topic models
Classifying text
Data preparation
LASSO model
Additional quantitative analysis
Summary
Creating a Package
Creating a new package
Summary
Other Books You May Enjoy
Index