Models Demystified

A Practical Guide from Linear Regression to Deep Learning

AvMichael Clark,Seth Berry

Inbunden, Engelska, 2025

Del i serien Chapman & Hall/CRC Data Science Series

1 162 kr

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Beskrivning

Unlock the Power of Data Science and Machine LearningIn this comprehensive guide, we delve into the world of data science, machinelearning, 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.

Produktinformation

Utgivningsdatum:2025-08-14
Mått:156 x 234 x 31 mm
Vikt:1 070 g
Format:Inbunden
Språk:Engelska
Serie:Chapman & Hall/CRC Data Science Series
Antal sidor:474
Förlag:Taylor & Francis Ltd
ISBN:9781032582580

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Mer om författaren

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.

Innehållsförteckning

1.Introduction2.Thinking About Models3.The Foundation4.Understanding the Model5.Understanding the Features6.Model Estimation and Optimization7.Estimating Uncertainty8.Generalized Linear Models9.Extending the Linear Model10.Core Concepts in Machine Learning11.Comon Models in Machine Learning12.Extending Machine Learning13.Causal Modeling14.Dealing with Data15.Danger Zone16.Parting Thoughts