The Many Cultures Of Learning From Data
In less than 100 pages, Modeling Mindsets elucidates the worldviews behind various statistical modeling and machine learning mindsets.
"”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
Where To Buy
Books on modeling often jump right into math and methods. Drowned in detail, it can take years to appreciate the assumptions and limitations of the various modeling mindsets. Written in a clear and concise style, Modeling Mindsets introduces approaches such as Bayesian inference, supervised learning, causal inference, and more.
After reading this book, you will have a much better understanding of the different approaches to modeling and be able to choose the right one for your problem.
Who this book is for
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.
You will get the most out of Modeling Mindsets if you keep an open mind You have to challenge the rigid assumptions of the mindset that feels natural to you.
Table Of Contents
- Statistical Modeling – Reason Under Uncertainty
- Frequentism - Infer “True” Parameters
- Bayesianism – Update Parameter Distributions
- Likelihoodism – Likelihood As Evidence
- Causal Inference – Identify And Estimate Causes
- Machine Learning – Learn Algorithms From Data
- Supervised Learning – Predict New Data
- Unsupervised Learning – Find Hidden Patterns
- Reinforcement Learning – Learn To Interact
- Deep Learning - Learn End-To-End Networks
- The T-Shaped Modeler
Looks like I got myself an early Xmas present @ChristophMolnar!— Sebastian Raschka (@rasbt) November 27, 2022
Gonna be a nice, pocketable airplane companion for the trip to #NeurIPS2022 next week!
(I’ll let you all know what I think! 😊) pic.twitter.com/SQBrbgDzvb
This is an article on Different Modeling Mindsets used across Academic and Industrial Research, based on the book Modeling Mindsets by @ChristophMolnar.#modeling #Mindset #DataScience https://t.co/ivLh9sRoZ5— Prasad Kulkarni (@pmka1991) December 27, 2022
Just finished reading a book: Modeling Mindsets by Christoph Molnar.— Daniel Akhabue #MillenniumFellowship (@doa_apprentice) January 17, 2023
And I can say for a fact that this book is an insightful read for all Data Scientists out there.
The book introduces you to the various mindsets held when building ML models.@ChristophMolnar @Nasereliver pic.twitter.com/HI37Hl2fpD
Looking forward to @ChristophMolnar's email course on conformal prediction 👇— Florian Wolf (@fwolf_mergeflow) December 13, 2022
And you should really get his book, "Modeling Mindsets":https://t.co/a6JLDay2aG
It's very well-written, to the point, and very helpful. https://t.co/Rw2z4N51te
Linear regression is machine learning…sometimes.— Juan Mateos Garcia (@JMateosGarcia) December 18, 2022
Christoph Molnar’s “Modeling Mindsets” starts strong. pic.twitter.com/Rw9eJXnP4F
Fun starter so far. I'm eager to learn and hopefully finally grasp what all the fuzz about bayesian vs frequentist is about pic.twitter.com/ekSzdNTRmy— Benno Krojer (resting) (@benno_krojer) May 18, 2023
Started my yearly review 😊. Wow, apparently, I read 29 books in 2022 ...— Sebastian Raschka (@rasbt) December 17, 2022
Some of the more technical ones 😅:
- Designing ML Systems
- The Kaggle Book
- NLP w Transformers
- The Book of Why
- Modeling Mindsets
Let me know if you would like to hear more about them, happy to chat!
If you want to get into machine learning like, for real, stop reading useless threads about prompt engineering or whatever nonsense and read @ChristophMolnar modelling mindsets book. It's like, you know, an *actual* book written by someone who actually knows about this stuff. https://t.co/UnDN7BWDdN— Alejandro Piad Morffis (@alepiad) May 9, 2023
1. The Elements of Statistical Learning. Written by inventors of CART, GBM, RF.— ℝyan Lucas (@RyanDLucas) January 17, 2023
2. Learn Python the Hard way. Because learn by doing and all that.
3. @ChristophMolnar’s Modelling Mindsets - gives a nice lay of the land for stats/ML that I feel would be useful for a beginner.
I've just finished "Modelling Mindsets" of @ChristophMolnar. It is a highly recommended book. Finally a book that organizes in a very accurate way the different ways/schools of thought that we have to deal with data. Their weaknesses and strengths. Instructive and clear. Thanks! pic.twitter.com/ZsY5nsrkcu— Carlos (@coforfe) December 6, 2022