This is the code repository for Machine Learning with R Quick Start Guide, published by Packt.
A beginner's guide to implementing machine learning techniques from scratch using R 3.5
This book is ideal for people wanting to get up-and-running with the core concepts of machine learning using R 3.5. This book follows a step-by-step approach to implementing an end-to-end pipeline, addressing data collection and processing, various types of data analysis, and machine learning use cases.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
myfiles <- list.files(path = "../MachineLearning/Banks_model/Data", pattern
= "20", full.names = TRUE)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| All | RStudio, R 3.5 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Iván Pastor Sanz is a lead data scientist and machine learning enthusiast with extensive experience in finance, risk management, and credit risk modeling. Iván has always endeavored to find solutions to make banking more comprehensible, accessible, and fair. Thus, in his thesis to obtain his PhD in economics, Iván tried to identify the origins of the 2008 financial crisis and suggest ways to avoid a similar crisis in the future.
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If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.