I guess what I am looking for is advice from practitioners who wont lead people astray who are really interested in diving deep into ML.
I have attempted to read the Statistical Learning book, and its so daunting because the book expects a lot of background knowledge, and it takes a while to really wrap your head around these concepts. I think people should learn from a lighter book, before diving into these books if you are lacking the background.
My current approach to pursuing a career in DL and ML is going to graduate school, taking a graduate ML course, and trying to apply my knowledge to different problems I am interested in.
I am reading the Bishop book Pattern Recognition now. I think from the perspective of having to re-learn a lot of calculus and probability, that book is more approachable than Statistical learning.
My advice (which I am attempting now) to dive deep into ML is follows:
1. Taking Bayesian ML class (at Cornell)
2. Read/Study Pattern Recognition by Bishop, for 5hrs/day
3. Try exercises, if fail, review solutions
4. If lost(which is usually), review missing concepts from MIT OCW scholar courses
I have attempted to read the Statistical Learning book, and its so daunting because the book expects a lot of background knowledge, and it takes a while to really wrap your head around these concepts. I think people should learn from a lighter book, before diving into these books if you are lacking the background.
My current approach to pursuing a career in DL and ML is going to graduate school, taking a graduate ML course, and trying to apply my knowledge to different problems I am interested in.
I am reading the Bishop book Pattern Recognition now. I think from the perspective of having to re-learn a lot of calculus and probability, that book is more approachable than Statistical learning.
My advice (which I am attempting now) to dive deep into ML is follows:
1. Taking Bayesian ML class (at Cornell) 2. Read/Study Pattern Recognition by Bishop, for 5hrs/day 3. Try exercises, if fail, review solutions 4. If lost(which is usually), review missing concepts from MIT OCW scholar courses