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The Probability -Data Science-ML space is so confusing. Any resources that would help clarify?

• muraiki 4y ago
  > 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. https://book.modeling-mindsets.com/ https://book.modeling-mindsets.com/ Edit: Ah darn, I forgot about this part: "You should feel comfortable with at least one of the mindsets in this book". So perhaps not the best start if you don't have a base in at least one method. For frequentist statistics, consider https://www.openintro.org/book/os/ https://www.openintro.org/book/os/
• sdl 4y ago
  "Probabilistic Machine Learning" by Kevin Murphy is all you need. ... only half joking ;) https://probml.github.io/pml-book/ https://probml.github.io/pml-book/