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Table Of Contents

Machine Learning with R Quick Start Guide

By : Sanz

5 (1)

Buy this Book

Machine Learning with R Quick Start Guide

5 (1)

By: Sanz

Buy this Book

Overview of this book

Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R.

Preface

Who this book is for

What this book covers

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Free Chapter

R Fundamentals for Machine Learning

R and RStudio installation

Some basic commands

Objects, special cases, and basic operators in R

Controlling code flow

All about R packages

Taking further steps

Summary

Predicting Failures of Banks - Data Collection

Collecting financial data

Collecting the target variable

Structuring data

Summary

Predicting Failures of Banks - Descriptive Analysis

Data overview

Implementing descriptive analysis

Summary

Predicting Failures of Banks - Univariate Analysis

Feature selection algorithm

Filter methods

Wrapper methods

Embedded methods

Dimensionality reduction

Summary

Predicting Failures of Banks - Multivariate Analysis

Logistic regression

Regularized methods

Testing a random forest model

Gradient boosting

Deep learning in neural networks

Support vector machines

Ensembles

Automatic machine learning

Summary

Visualizing Economic Problems in the European Union

A general overview of economic problems in countries

Clustering countries based on macroeconomic imbalances

Summary

Sovereign Crisis - NLP and Topic Modeling

Predicting country ratings using macroeconomic information

Implementing decision trees

Predicting sovereign ratings using European country reports

Summary

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Wrapper methods

As stated at the beginning of this section, wrapper methods evaluate subsets of variables to detect the possible interactions between variables being a step ahead of the filter methods.

In wrapper methods, several combinations of variables are used in a predictive model and a score is given to each combination according to the model accuracy.

In wrapper methods, a classifier is iteratively trained with multiple combinations of variables acting as a black box, for which the only output is a ranking of important features.