Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications

Author:   Brindha Priyadarshini Jeyaraman ,  Ludvig Renbo Olsen ,  Monicah Wambugu
Publisher:   Packt Publishing Limited
ISBN:  

9781838550134


Pages:   416
Publication Date:   30 August 2019
Format:   Paperback
Availability:   Available To Order   Availability explained
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Practical Machine Learning with R: Define, build, and evaluate machine learning models for real-world applications


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Author:   Brindha Priyadarshini Jeyaraman ,  Ludvig Renbo Olsen ,  Monicah Wambugu
Publisher:   Packt Publishing Limited
Imprint:   Packt Publishing Limited
ISBN:  

9781838550134


ISBN 10:   1838550135
Pages:   416
Publication Date:   30 August 2019
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Available To Order   Availability explained
We have confirmation that this item is in stock with the supplier. It will be ordered in for you and dispatched immediately.

Table of Contents

Table of Contents An Introduction to Machine Learning Data Cleaning and Pre-Processing Feature Engineering Introduction to neuralnet and Evaluation Methods Linear and Logistic Regression Models Unsupervised Learning

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Author Information

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. This book provides a great platform for her to share the knowledge she has gained over the past few years of working in data science and machine learning. 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. Monicah Wambugu is the lead data scientist at 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 socioeconomic needs of target audiences.

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