Book Image

Mastering Machine Learning with R - Third Edition

By : Cory Lesmeister

Book Image

Mastering Machine Learning with R - Third Edition

By: Cory Lesmeister

Overview of this book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Preparing and Understanding Data

Preparing and Understanding Data

Reading the data

Handling duplicate observations

Handling missing values

Zero and near-zero variance features

Treating the data

Linear Regression

Linear Regression

Univariate linear regression

Multivariate linear regression

Logistic Regression

Logistic Regression

Classification methods and linear regression

Logistic regression

Model training and evaluation

Advanced Feature Selection in Linear Models

Advanced Feature Selection in Linear Models

Regularization overview

Modeling and evaluation

K-Nearest Neighbors and Support Vector Machines

K-Nearest Neighbors and Support Vector Machines

K-nearest neighbors

Support vector machines

Manipulating data

Modeling and evaluation

Tree-Based Classification

Tree-Based Classification

An overview of the techniques

Datasets and modeling

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Introduction to neural networks

Deep learning – a not-so-deep overview

Creating a simple neural network

An example of deep learning

Creating Ensembles and Multiclass Methods

Creating Ensembles and Multiclass Methods

Data understanding

Modeling and evaluation

Cluster Analysis

Cluster Analysis

Hierarchical clustering

K-means clustering

Dataset background

Data understanding and preparation

Principal Component Analysis

Principal Component Analysis

An overview of the principal components

Association Analysis

Association Analysis

An overview of association analysis

Data understanding

Data preparation

Modeling and evaluation

Time Series and Causality

Time Series and Causality

Univariate time series analysis

Time series data

Modeling and evaluation

Text Mining

Text mining framework and methods

Sentiment analysis

Classifying text

Additional quantitative analysis

Creating a Package

Creating a Package

Creating a new package

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Data understanding

For our business case, we'll focus on identifying the association rules for a grocery store. The dataset will be from the arules package and is called Groceries. This dataset consists of actual transactions over 30 days from a real-world grocery store and consists of 9,835 different purchases. All of the items purchased are put into one of 169 categories, for example, bread, wine, meat, and so on.

Let's say that we want to develop an understanding of what potential customers will purchase along with beer to identify the right product placement within the store or support a cross-selling campaign.