, Models Demystified
Unlock the Power of Data Science and Machine Learning
In this comprehensive guide, we delve into the world of data science, machine
learning, and AI modeling, providing readers with a robust foundation and prac-
tical skills to tackle real-world problems. From basic modeling techniques to ad-
vanced machine learning algorithms, this book covers a wide range of topics,
ensuring that readers at all levels can benefit from its content. Each chapter is
meticulously crafted to offer clear explanations, hands-on examples, and code
snippets in both Python and R, making complex concepts accessible and action-
able. Additional focus is placed on model interpretation and estimation, common
data issues, modeling pitfalls to avoid, and best practices for modeling in general.
Michael Clark is a senior machine learning scientist for OneSix, and in prior
stints, was a data science consultant at the University of Michigan and Notre
Dame. His models have been used in production across a variety of industries,
and can be seen in dozens of publications across several academic disciplines. He
has a passion for helping people of all skill levels learn difficult stuff.
Seth Berry is the Academic Co-Director of the Master of Science in Business
Analytics (MSBA) Residential Program, and Associate Teaching Professor at the
University of Notre Dame for the IT, Analytics, and Operations Department. He
has a PhD in Applied Experimental Psychology, and has been teaching and con-
sulting in data science for over a decade.
,CHAPMAN & HALL/CRC DATA SCIENCE SERIES
Reflecting the interdisciplinary nature of the field, this book series brings together researchers, practi-
tioners, and instructors from statistics, computer science, machine learning, and analytics. The series
will publish cutting-edge research, industry applications, and textbooks in data science.
The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of
the series includes titles in the areas of machine learning, pattern recognition, predictive analytics,
business analytics, Big Data, visualization, programming, software, learning analytics, data wran-
gling, interactive graphics, and reproducible research.
Recently Published Titles
Data Science for Sensory and Consumer Scientists
Thierry Worch, Julien Delarue, Vanessa Rios De Souza and John Ennis
Data Science in Practice
Tom Alby
Introduction to NFL Analytics with R
Bradley J. Congelio
Soccer Analytics
An Introduction Using R
Clive Beggs
Spatial Statistics for Data Science
Theory and Practice with R
Paula Moraga
Research Software Engineering
A Guide to the Open Source Ecosystem
Matthias Bannert
The Data Preparation Journey
Finding Your Way With R
Martin Hugh Monkman
Getting (more out of) Graphics
Practice and Principles of Data Visualisation
Antony Unwin
Introduction to Data Science
Data Wrangling and Visualization with R Second Edition
Rafael A. Irizarry
Data Science
A First Introduction with Python
Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom and Lindsey Heagy
Mathematical Engineering of Deep Learning
Benoit Liquet, Sarat Moka, and Yoni Nazarathy
Introduction to Classifier Performance Analysis with R
Sutaip L.C. Saw
For more information about this series, please visit: https://www.routledge.com/Chapman--Hall-
CRC-Data-Science-Series/book-series/CHDSS
, Models Demystified
A Practical Guide from Linear
Regression to Deep Learning
Michael Clark and Seth Berry
Unlock the Power of Data Science and Machine Learning
In this comprehensive guide, we delve into the world of data science, machine
learning, and AI modeling, providing readers with a robust foundation and prac-
tical skills to tackle real-world problems. From basic modeling techniques to ad-
vanced machine learning algorithms, this book covers a wide range of topics,
ensuring that readers at all levels can benefit from its content. Each chapter is
meticulously crafted to offer clear explanations, hands-on examples, and code
snippets in both Python and R, making complex concepts accessible and action-
able. Additional focus is placed on model interpretation and estimation, common
data issues, modeling pitfalls to avoid, and best practices for modeling in general.
Michael Clark is a senior machine learning scientist for OneSix, and in prior
stints, was a data science consultant at the University of Michigan and Notre
Dame. His models have been used in production across a variety of industries,
and can be seen in dozens of publications across several academic disciplines. He
has a passion for helping people of all skill levels learn difficult stuff.
Seth Berry is the Academic Co-Director of the Master of Science in Business
Analytics (MSBA) Residential Program, and Associate Teaching Professor at the
University of Notre Dame for the IT, Analytics, and Operations Department. He
has a PhD in Applied Experimental Psychology, and has been teaching and con-
sulting in data science for over a decade.
,CHAPMAN & HALL/CRC DATA SCIENCE SERIES
Reflecting the interdisciplinary nature of the field, this book series brings together researchers, practi-
tioners, and instructors from statistics, computer science, machine learning, and analytics. The series
will publish cutting-edge research, industry applications, and textbooks in data science.
The inclusion of concrete examples, applications, and methods is highly encouraged. The scope of
the series includes titles in the areas of machine learning, pattern recognition, predictive analytics,
business analytics, Big Data, visualization, programming, software, learning analytics, data wran-
gling, interactive graphics, and reproducible research.
Recently Published Titles
Data Science for Sensory and Consumer Scientists
Thierry Worch, Julien Delarue, Vanessa Rios De Souza and John Ennis
Data Science in Practice
Tom Alby
Introduction to NFL Analytics with R
Bradley J. Congelio
Soccer Analytics
An Introduction Using R
Clive Beggs
Spatial Statistics for Data Science
Theory and Practice with R
Paula Moraga
Research Software Engineering
A Guide to the Open Source Ecosystem
Matthias Bannert
The Data Preparation Journey
Finding Your Way With R
Martin Hugh Monkman
Getting (more out of) Graphics
Practice and Principles of Data Visualisation
Antony Unwin
Introduction to Data Science
Data Wrangling and Visualization with R Second Edition
Rafael A. Irizarry
Data Science
A First Introduction with Python
Tiffany Timbers, Trevor Campbell, Melissa Lee, Joel Ostblom and Lindsey Heagy
Mathematical Engineering of Deep Learning
Benoit Liquet, Sarat Moka, and Yoni Nazarathy
Introduction to Classifier Performance Analysis with R
Sutaip L.C. Saw
For more information about this series, please visit: https://www.routledge.com/Chapman--Hall-
CRC-Data-Science-Series/book-series/CHDSS
, Models Demystified
A Practical Guide from Linear
Regression to Deep Learning
Michael Clark and Seth Berry
