IN THIS CHAPTER
Figuring out why you need a matrix
Computing with matrix calculus to your advantage
Getting a glance at how probability works
Explaining the Bayesian point of view on probability
Describing observations using statistical measures
“With me, everything turns into mathematics.”
— RENÉ DESCARTES
If you want to implement existing machine learning algorithms from scratch or you need to devise new ones, you will require some knowledge of probability, linear algebra, linear programming, and multivariable calculus. You also need to know how to translate math into working code. This chapter begins by helping you understand the mechanics of machine learning math and describes how to translate math basics into usable code.
If you want to apply existing machine learning for practical purposes instead, you can leverage existing R and Python software libraries using a basic knowledge of math and statistics. In the end, you ...