Mastering Machine Learning with R

By : Lesmeister

1.3 (3)

Buy this Book

Mastering Machine Learning with R

1.3 (3)

By: Lesmeister

Buy this Book

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

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Free Chapter

Preparing and Understanding Data

Overview

Reading the data

Handling duplicate observations

Handling missing values

Zero and near-zero variance features

Treating the data

Summary

Linear Regression

Univariate linear regression

Multivariate linear regression

Summary

Logistic Regression

Classification methods and linear regression

Logistic regression

Model training and evaluation

Summary

Advanced Feature Selection in Linear Models

Regularization overview

Data creation

Modeling and evaluation

Summary

K-Nearest Neighbors and Support Vector Machines

K-nearest neighbors

Support vector machines

Manipulating data

Modeling and evaluation

Summary

Tree-Based Classification

An overview of the techniques

Datasets and modeling

Summary

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

Summary

Creating Ensembles and Multiclass Methods

Ensembles

Data understanding

Modeling and evaluation

Summary

Cluster Analysis

Hierarchical clustering

K-means clustering

Gower and PAM

Random forest

Dataset background

Data understanding and preparation

Modeling

Summary

Principal Component Analysis

An overview of the principal components

Data

PCA modeling

Summary

Association Analysis

An overview of association analysis

Data understanding

Data preparation

Modeling and evaluation

Summary

Time Series and Causality

Univariate time series analysis

Time series data

Modeling and evaluation

Summary

Text Mining

Text mining framework and methods

Data overview

Word frequency

Sentiment analysis

N-grams

Topic models

Classifying text

Additional quantitative analysis

Summary

Creating a Package

Creating a new package

Summary

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Logistic Regression

"The true logic of this world is the calculus of probabilities."

- James Clerk Maxwell, Scottish physicist

In the previous chapter, we took a look at using Ordinary Least Squares (OLS) to predict a quantitative outcome or, in other words, linear regression. It's now time to shift gears somewhat and examine how we can develop algorithms to predict qualitative outcomes. Such outcome variables could be binary (male versus female, purchase versus doesn't purchase, or a tumor is benign versus malignant) or multinomial categories (education level or eye color). Regardless of whether the outcome of interest is binary or multinomial, our task is to predict the probability of an observation belonging to a particular category of the outcome variable. In other words, we develop an algorithm to classify the observations.

To begin exploring classification...