Machine Learning with R Quick Start Guide - Tapa blanda

Reseña del editor

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more.

Key Features

• Use R 3.5 to implement real-world examples in machine learning
• Implement key machine learning algorithms to understand the working mechanism of smart models
• Create end-to-end machine learning pipelines using modern libraries from the R ecosystem

Book Description

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.

What you will learn

• Introduce yourself to the basics of machine learning with R 3.5
• Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your results
• Learn to build predictive models with the help of various machine learning techniques
• Use R to visualize data spread across multiple dimensions and extract useful features
• Use interactive data analysis with R to get insights into data
• Implement supervised and unsupervised learning, and NLP using R libraries

Who this book is for

This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

Table of Contents

1. R Fundamentals for Machine Learning
2. Predicting Failures of Banks - Data Collection
3. Predicting Failures of Banks - Descriptive Analysis
4. Predicting Failures of Banks - Univariate Analysis
5. Predicting the Failures of Banks - A Multivariate Analysis
6. Visualizing Economic Problems in the European Union
7. Sovereign Crisis - NLP and Topic Modeling

Biografía del autor

Ivan Pastor Sanz is a lead data scientist and machine learning enthusiast with extensive experience in finance, risk management, and credit risk modeling. Ivan has always endeavored to find solutions to make banking more comprehensible, accessible, and fair. Thus, in his thesis to obtain his PhD in economics, Ivan tried to identify the origins of the 2008 financial crisis and suggest ways to avoid a similar crisis in the future.