ML engineer here. I didn’t take any ML classes in college and picked up most of what I know on the job.
I think this advice is directionally correct - reading through a theory-dense textbook like Bishop, which many consider to be a foundational ML textbook, is likely to be a bad use of your time. However, I think it does help to start with some theory, if only to give you the vocabulary with which to think about and get help with issues that you run into. At the risk of sounding like a broken record, Andrew Ng’s class on Coursera (https://www.coursera.org/learn/machine-learning) is quite good - it’s accessible with a bit of basic calculus knowledge (simple single variable derivatives and partial derivatives are all you need) and basic linear algebra (like, matrix multiplication). The whole class took me around 30 hours to get through, so if you’re determined, you could probably finish it in 2-3 weeks even if you’re pretty busy.
Also, if you like having text notes to refer to, I made these notes for myself a few years back when taking the class: https://github.com/tlv/ml_ng. There are some spots where, for my own understanding (I’m a bit of a stickler for mathematical rigor), I added more of the reasoning/equation pushing that Ng glosses over in his lectures. I would say that for a practical understanding of how to apply the concepts covered in the class, there’s no need to read those parts carefully (there’s a reason why Ng glossed over them).
But yeah, to all the people saying you should start by reading entire textbooks on multivariable calculus, statistics, and linear algebra...that’s not necessary. Most ML engineers I’ve met (and even most industry researchers, although my sample size there is much smaller) don’t understand all of those things that deeply.
Also, one last semi-related note - if you’re reading a paper and get intimidated by some really complex math, oftentimes that math is just included to make the paper look more impressive, and sometimes it’s not even correct.
I think this advice is directionally correct - reading through a theory-dense textbook like Bishop, which many consider to be a foundational ML textbook, is likely to be a bad use of your time. However, I think it does help to start with some theory, if only to give you the vocabulary with which to think about and get help with issues that you run into. At the risk of sounding like a broken record, Andrew Ng’s class on Coursera (https://www.coursera.org/learn/machine-learning) is quite good - it’s accessible with a bit of basic calculus knowledge (simple single variable derivatives and partial derivatives are all you need) and basic linear algebra (like, matrix multiplication). The whole class took me around 30 hours to get through, so if you’re determined, you could probably finish it in 2-3 weeks even if you’re pretty busy.
Also, if you like having text notes to refer to, I made these notes for myself a few years back when taking the class: https://github.com/tlv/ml_ng. There are some spots where, for my own understanding (I’m a bit of a stickler for mathematical rigor), I added more of the reasoning/equation pushing that Ng glosses over in his lectures. I would say that for a practical understanding of how to apply the concepts covered in the class, there’s no need to read those parts carefully (there’s a reason why Ng glossed over them).
But yeah, to all the people saying you should start by reading entire textbooks on multivariable calculus, statistics, and linear algebra...that’s not necessary. Most ML engineers I’ve met (and even most industry researchers, although my sample size there is much smaller) don’t understand all of those things that deeply.
Also, one last semi-related note - if you’re reading a paper and get intimidated by some really complex math, oftentimes that math is just included to make the paper look more impressive, and sometimes it’s not even correct.