Models Demystified
A Practical Guide from Linear Regression to Deep Learning
by Michael Clark & Seth Berry

This book is designed to guide readers on a comprehensive journey into the world of data science and modeling. For those just beginning their exploration, it offers:
- A solid foundation in the basics of modeling, presented from a practical and accessible perspective.
- A versatile toolkit of models and concepts that can be immediately applied to real-world problems.
- A balanced approach that integrates both statistical and machine learning methodologies.

For readers already experienced in modeling, the book provides:
- Deeper context and insights into familiar models.
- An introduction to new and advanced models that expand your knowledge.
- Enhanced understanding of how to select the most appropriate model for a given task and where to focus your efforts.

Above all, this book aims to highlight the common threads that connect different models, offering readers a clear and intuitive understanding of how they function and interrelate. Whether you're a beginner or a seasoned practitioner, this resource is crafted to deepen your expertise and broaden your perspective on the art and science of modeling.

Table of contents:

Preface
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  Common Models in Machine Learning
12  Extending Machine Learning
13  Causal Modeling
14  Dealing with Data
15  Danger Zone
16  Parting Thoughts

Link: Site

Navigational hashtags: #armknowledgesharing #armbooks #armsites
General hashtags: #ml #machinelearning #r #python #linear #metrics #featureengineering #optimization #mle #glm #dl #deeplearning

@data_science_weekly