> Would Elements of Statistical Learning be my next book?
Honestly no I don't think so. ESL is likely too advanced.
I would use that screenshot I posted above as a litmus test. Do you understand that notation? The `E` with the subscript? And why they're using `trace[]`? If you do, then you can likely follow ESL. If not -- which would be understandable because even early undergrads likely can't -- then I say you shouldn't try and follow ISL up directly with ESL. It really is a graduate text.
> I’ve seen the Bishop book highly recommended too, and it has been mentioned in this post.
Bishop has a similar problem: [1]. I had to scroll to chapter 2 for this screenshot (pg. 83), but it really is par for the course.
So, and this is totally my opinion here so YMMV, recommendations for foundational ML info tend to be wildly too advanced for the people seeking them out. I'm a math-y person. I really like learning about the math foundations of ML. But ML builds on a lot of other concepts and you can't just jump into the deep end. In my opinion, ML foundations should come at the end of a lengthy sequence of math and statistics courses. Students will just be too lost without them.
I don't mean to be discouraging here. I think nearly anyone who's willing to put in the time can learn this stuff! But here's a more reasonable sequence I found on reddit a while back that would set someone up nicely for being able to follow ESL: [2]. Without the proper foundation, it's just too difficult to follow ESL or Bishop IMO.
Last, I'll note that you don't need to understand the nitty-gritty of ML math to be an ML practitioner. In fact, I'd argue that taking the effort would be distracting because 1) a basic understanding (like you'd get from working through ISL) is probably good enough to start messing with libraries and 2) practitioners need a whole bunch of other knowledge (like general software skills and how to maintain ML datasets) that they also have to take the time to learn.
It has been a while, but I do understand Σ, e^x, ln, matrices, vectors, etc.
However, like you mentioned, you don't need to work through the proofs to understand logistic regression, lasso, ridge regression, and bootstrapping, for example.
Honestly no I don't think so. ESL is likely too advanced.
I would use that screenshot I posted above as a litmus test. Do you understand that notation? The `E` with the subscript? And why they're using `trace[]`? If you do, then you can likely follow ESL. If not -- which would be understandable because even early undergrads likely can't -- then I say you shouldn't try and follow ISL up directly with ESL. It really is a graduate text.
> I’ve seen the Bishop book highly recommended too, and it has been mentioned in this post.
Bishop has a similar problem: [1]. I had to scroll to chapter 2 for this screenshot (pg. 83), but it really is par for the course.
So, and this is totally my opinion here so YMMV, recommendations for foundational ML info tend to be wildly too advanced for the people seeking them out. I'm a math-y person. I really like learning about the math foundations of ML. But ML builds on a lot of other concepts and you can't just jump into the deep end. In my opinion, ML foundations should come at the end of a lengthy sequence of math and statistics courses. Students will just be too lost without them.
I don't mean to be discouraging here. I think nearly anyone who's willing to put in the time can learn this stuff! But here's a more reasonable sequence I found on reddit a while back that would set someone up nicely for being able to follow ESL: [2]. Without the proper foundation, it's just too difficult to follow ESL or Bishop IMO.
Last, I'll note that you don't need to understand the nitty-gritty of ML math to be an ML practitioner. In fact, I'd argue that taking the effort would be distracting because 1) a basic understanding (like you'd get from working through ISL) is probably good enough to start messing with libraries and 2) practitioners need a whole bunch of other knowledge (like general software skills and how to maintain ML datasets) that they also have to take the time to learn.
[1] https://imgur.com/uXWZ6Bv
[2] https://www.reddit.com/r/learnmachinelearning/comments/ggpzk...