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ng Mindsets elucidates the worldviews behind various statistical modeling and machine learning mindsets.

"Modeling Mindsets offers a compact but informative overview over the different approaches, assumptions and goals when working with data. I strongly recommend gifting this book to your present and future colleagues to get the most out of your collaboration."

- Sebastian Raschka, Machine learning & AI researcher

About The Book

Most books on modeling dive straight into the math and methodologies, leaving readers struggling to grasp the underlying assumptions and limitations. Written in a clear and concise style, Modeling Mindsets introduces approaches such as Bayesian inference, supervised learning, causal inference, and more.

With this book, you'll gain a deeper understanding of different modeling techniques, empowering you to choose the right one for your problem.

"It has taken me many years of fumbling around with ML and statistics to achieve a fraction of the intuition in the book. Save yourself the time!"

- Robert Martin

Table of Contents

1. Introduction
2. Statistical Modeling - Reason Under Uncertainty
3. Frequentism - Infer "True" Parameters
4. Bayesianism - Update Parameter Distributions
5. Likelihoodism - Likelihood As Evidence
6. Causal Inference - Identify And Estimate Causes
7. Machine Learning - Learn Algorithms From Data
8. Supervised Learning - Predict New Data
9. Unsupervised Learning - Find Hidden Patterns
10. Reinforcement Learning - Learn To Interact
11. Deep Learning - Learn End-To-End Networks
12. The T-Shaped Modeler

Who This Book Is ForModeling Mindsets is designed for data scientists, statisticians, machine learners, and quantitative researchers who are already familiar with modeling and working with data. To maximize your learning experience:

This book is for everyone who builds models from data: data scientists, statisticians, machine learners, and quantitative researchers. To get the most out of this book:

• You should already have experience with modeling and working with data.
• You should feel comfortable with at least one of the mindsets in this book.

Don't read this book if:

• You are completely new to working with data and models.
• You cling to the mindset you already know and aren't open to other mindsets.

Embrace the full potential of Modeling Mindsets by challenging your assumptions and being open to diverse perspectives.

This compact guide is perfect for data-driven professionals looking to enhance their understanding of modeling approaches, assumptions, and goals when working with data. By delving into this book, you'll save years of trial and error, foster intellectual growth, and improve collaboration with your colleagues. Gain valuable insights into Bayesian inference, supervised learning, causal inference, and more, and elevate your data analysis skills by choosing the right modeling approach for your problem. Make the most of your data-modeling journey with "Modeling Mindsets."