Modeling Mindsets: The Many Cultures Of Learning From Data

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

• # Probability & Statistics (Books)