Models Demystified
eBook - ePub

Models Demystified

A Practical Guide from Linear Regression to Deep Learning

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Models Demystified

A Practical Guide from Linear Regression to Deep Learning

About this book

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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Yes, you can access Models Demystified by Michael Clark,Seth Berry in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2025
eBook ISBN
9781040385401
Edition
0
Topic
Mathematics
Index
Mathematics

Table of contents

  1. Cover Page
  2. Half-Title Page
  3. Series Page
  4. Title Page
  5. Copyright Page
  6. Table of contents
  7. Preface
  8. 1 Introduction
  9. 2 Thinking About Models
  10. 3 The Foundation
  11. 4 Understanding the Model
  12. 5 Understanding the Features
  13. 6 Model Estimation and Optimization
  14. 7 Estimating Uncertainty
  15. 8 Generalized Linear Models
  16. 9 Extending the Linear Model
  17. 10 Core Concepts in Machine Learning
  18. 11 Common Models in Machine Learning
  19. 12 Extending Machine Learning
  20. 13 Causal Modeling
  21. 14 Dealing with Data
  22. 15 Danger Zone
  23. 16 Parting Thoughts
  24. A Acknowledgments
  25. B Matrix Operations
  26. C Dataset Descriptions
  27. References
  28. Index