Michael Clark, Seth Berry

Models Demystified (eBook, ePUB)

A Practical Guide from Linear Regression to Deep Learning

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 492
• Erscheinungstermin: 15. August 2025
• Englisch
• ISBN-13: 9781040385401
• Artikelnr.: 74923818

Autorenporträt

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.

Inhaltsangabe

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