Bifalgorithm post

I'm sorry, but why does no one talk about how much math actually goes into machine learning? And it's about 35% linear algebra, 35% probability in stats and 30% calculus. For linear algebra, you can use this course right here. It is from MIT and it's completely free, gets you a good understanding of things like vectors and matrix multiplication. For calculus use this resource right here and it walks you through single and multivariable calculus as well as differential equations, which is basically solving for the value that depends on the rate of change of something else. For probability and stats, a good resource is this Intro to Probability course taught by Harvard professors and the PDF for this is completely free. But if you want to learn the math behind the algorithms, use this book right here. It walks you through all the math and how to apply it to your code. I've used all these resources and they're honestly pretty good, but they don't always cater to machine learning so you can spend a ton of time just going through fluff trying to find exactly what you need. So I built an app where you can learn everything you need to know about machine learning in one singular place. I'm about to launch so come at math if you want access to the app and follow for more.

I'm sorry, but why does no one talk about how much math actually goes into machine learning? And it's about 35% linear algebra, 35% probability in stats and 30…