Models Demystified
A Practical Guide from Linear Regression to Deep Learning
1st Edition
Published on 14. August 2025
Book
Hardback
474 pages
978-1-032-58258-0 (ISBN)
Description
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 practical skills to tackle real-world problems. From basic modeling techniques to advanced 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 actionable. Additional focus is placed on model interpretation and estimation, common data issues, modeling pitfalls to avoid, and best practices for modeling in general.
In this comprehensive guide, we delve into the world of data science, machine
learning, and AI modeling, providing readers with a robust foundation and practical skills to tackle real-world problems. From basic modeling techniques to advanced 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 actionable. Additional focus is placed on model interpretation and estimation, common data issues, modeling pitfalls to avoid, and best practices for modeling in general.
More details
Series
Language
English
Place of publication
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Academic
Illustrations
104 s/w Abbildungen, 104 s/w Zeichnungen
104 Line drawings, black and white; 104 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 31 mm
Weight
898 gr
ISBN-13
978-1-032-58258-0 (9781032582580)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Michael Clark | Seth Berry
Models Demystified
A Practical Guide from Linear Regression to Deep Learning
E-Book
08/2025
1st Edition
Chapman and Hall
€73.99
Available for download
Michael Clark | Seth Berry
Models Demystified
A Practical Guide from Linear Regression to Deep Learning
E-Book
08/2025
1st Edition
Chapman and Hall
€73.99
Available for download
Persons
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 consulting in data science for over a decade.
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 consulting in data science for over a decade.
Content
1.Introduction
2.Thinking About Models
3.The Foundation
4.Understanding the Model
5.Understanding the Features
6.Model Estimation and Optimization
7.Estimating Uncertainty
8.Generalized Linear Models
9.Extending the Linear Model
10.Core Concepts in Machine Learning
11.Comon Models in Machine Learning
12.Extending Machine Learning
13.Causal Modeling
14.Dealing with Data
15.Danger Zone
16.Parting Thoughts
2.Thinking About Models
3.The Foundation
4.Understanding the Model
5.Understanding the Features
6.Model Estimation and Optimization
7.Estimating Uncertainty
8.Generalized Linear Models
9.Extending the Linear Model
10.Core Concepts in Machine Learning
11.Comon Models in Machine Learning
12.Extending Machine Learning
13.Causal Modeling
14.Dealing with Data
15.Danger Zone
16.Parting Thoughts