Statistical Inference by Casella is great for self learning, not only because of the main text but because it has a huge number of exercises and a detailed solutions manual can be found online.
this is basically a graduate degree in statistics, with some optimization thrown in - the Lehman and Casella books are "biblical". it's funny that not a single one has "machine learning" in the name, which is something I've always suspected.
This is just more affiliate spam. If we're going to have reading lists like these posted can we at least have some context/learnings/observations beyond what the publishers write about the book themselves?
A person working with ML would have to sink in an incredible amount of time to go through all these books. Doubt I know anyone that read more than two of the listed books, even if I use ML on a daily basis.
Essential perhaps if you want to do research in Machine learning or work on developing new machine learning algorithms and libraries. Hardly essential if all you want to do is take a well understood algorithm from a well known ML package and apply it your data. What is essential then is knowing the relative strength and weaknesses of the different existing approaches and knowing which one to pick given your data and computational limitations. And as far as I can tell non of those books cover that.
That being said, the list is excellent for people who want a solid theoretical grounding of the underlying mathematics, and many of my favorite books are on the list.
This book covers everything from simple regression and classification from the statistical side to things like gradient boosted decision trees and the like on the ML side with enough math to make sure you understand what's actually going on.
I should note, it doesn't touch deep learning, which is what I suspect most people interested in 'machine learning' without any background in stats want to learn about these days.
11 comments
[ 1032 ms ] story [ 1130 ms ] threadThat being said, the list is excellent for people who want a solid theoretical grounding of the underlying mathematics, and many of my favorite books are on the list.
Edit: Thanks!
You can find accompanying code here: https://github.com/ageron/handson-ml
This book covers everything from simple regression and classification from the statistical side to things like gradient boosted decision trees and the like on the ML side with enough math to make sure you understand what's actually going on.
I should note, it doesn't touch deep learning, which is what I suspect most people interested in 'machine learning' without any background in stats want to learn about these days.