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Practical Machine Learning with R

You're reading from Practical Machine Learning with R Define, build, and evaluate machine learning models for real-world applications

Product type Paperback

Published in Aug 2019

Publisher Packt

ISBN-13 9781838550134

Length 416 pages

Edition 1st Edition

Languages

R

Tools

RStudio

Concepts

Machine Learning

Authors (3):

Brindha Priyadarshini Jeyaraman

Ludvig Renbo Olsen

Monicah Wambugu

View More author details

Table of Contents (8) Chapters

About the Book

1. An Introduction to Machine Learning FREE CHAPTER

2. Data Cleaning and Pre-processing

3. Feature Engineering

4. Introduction to neuralnet and Evaluation Methods

5. Linear and Logistic Regression Models

6. Unsupervised Learning

1. Appendix

Installing Libraries

Click on packages tab as shown below:
Figure 0.7: Packages in R Studio
Select the packages that needs to be installed. The package will be attached on the left as shown below:
Figure 0.8: Attaching the package MASS
Now the function of this library can be used. To install packages that are not displayed in the above, click on the Install option:
Figure 0.9: Install option
Type the package you would want to install. For instance, select ggplot2 and click Install:

Figure 0.10: Install packages pop-up

Figure 0.10: Install packages pop-up

The status of the installation of the package can be viewed in the console.

Alternatively, the install.packages("packagename") can also be used.

The rest of the chapter is locked

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Authors (3)

Jeyaraman

Jeyaraman

Brindha Priyadarshini Jeyaraman is a senior data scientist at AIDA Technologies. She has completed her M.Tech in knowledge engineering with a gold medal from the National University of Singapore. She has more than 10 years of work experience and she is an expert in understanding business problems, and designing and implementing solutions using machine learning. She has worked on several real data science projects in the insurance and finance domain.

See other products by Jeyaraman

Wambugu

Wambugu

Monicah Wambugu is the lead Data Scientist at Loanbee, a financial technology company that offers micro-loans by leveraging on data, machine learning and analytics to perform alternative credit scoring. She is a graduate student at the School of Information at UC Berkeley Masters in Information Management and Systems. Monicah is particularly interested in how data science and machine learning can be used to design products and applications that respond to the behavioral and socio-economic needs of target audiences.

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Olsen

Olsen

Ludvig Renbo Olsen, BSc in Cognitive Science from Aarhus University, is the author of multiple R packages, such as groupdata2 and cvms. With 4 years of R and Python experience, including working as a machine learning researcher at the Danish startup UNSILO, he is passionate about creating tools and tutorials for students and scientists. Guided by Effective Altruism, he intends to positively impact the world through his career.

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