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Practical Machine Learning with R

You're reading from Practical Machine Learning with R

Product type Book

Published in Aug 2019

Publisher Packt

ISBN-13 9781838550134

Pages 416 pages

Edition 1st Edition

Languages

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

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

Regression

Regression models are used to predict the value of a dependent variable from a set of independent variables, and to inform us about the strengths and forms of the potential relationships between each independent variable and the dependent variable in our dataset. While we will only cover linear and logistic regression in this chapter, it is worth noting that there are more types of regression, such as Poisson regression, for predicting count variables, such as the number of tattoos a person has, and ordinal regression, for predicting ranked variables, such as questionnaire answers ("Really Bad", "Bad", "Decent", "Good", "Really Good"), where the difference between "Decent" and "Good" is not necessarily the same as between "Really bad" and "Bad".

Each of these regression models relies on a set of assumptions about the data. For instance, in order to meaningfully use and interpret a linear regression...

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Authors (3)

Brindha Priyadarshini 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.

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Ludvig Renbo 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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Monicah 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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