Cory Lesmeister - Advanced Machine Learning with R

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First published: May 2019

Production reference: 2250719

Published by Packt Publishing Ltd.
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ISBN 978-1-83864-177-1

www.packtpub.com

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Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the Advanced Analytics team at Cummins, Inc. in Columbus, Indiana. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. Cory has a BBA in Aviation Administration from the University of North Dakota and a commercial helicopter license.

Dr. Sunil Kumar Chinnamgari has a Ph.D. in computer science (specializing in machine learning and natural language processing). He is an AI researcher with more than 14 years of industry experience. Currently, he works in the capacity of a lead data scientist with a US financial giant. He has published several research papers in Scopus and IEEE journals and is a frequent speaker at various meet-ups. He is an avid coder and has won multiple hackathons. In his spare time, Sunil likes to teach, travel, and spend time with family.

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R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics.

This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You'll tackle realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. You'll explore different clustering techniques to segment customers using wholesale data and use TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. Youll also be introduced to reinforcement learning along with its various use cases and models. Additionally, this book provides you with a glimpse into how some of these black-box models can be diagnosed and understood.

By the end of this Learning Path, youll be equipped with the skills you need to deploy machine learning techniques in your own projects.

If youre a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this Learning Path .

, Preparing and Understanding Data, covers the loading of data and demonstrates how to obtain an understanding of its structure and dimensions, as well as how to install the necessary packages.

, Linear Regression , provides you with a solid foundation before learning advanced methods such as Support Vector Machines and Gradient Boosting. No more solid foundation exists than the least squares linear regression.

, Logistic Regression , presents a discussion on how logistic regression and discriminant analysis is used in order to predict a categorical outcome. Multivariate adaptive regression splines have been added. This technique performs well, handles non-linearity, and is easy to explain.

, Advanced Feature Selection in Linear Models , shows regularization techniques to help improve the predictive ability and interpretability as feature selection is a critical and often extremely challenging component of machine learning. It also includes techniques not only for regression but also for a classification problem.