Demystifying the Math Behind Machine Learning

Summary

This chapter discusses the mechanics of machine learning math and describes how to translate math basics into usable code. Machine learning is so appealing because it allows machines to learn from real‐world examples and determine what such data implies. The mathematical and statistical basis of machine learning makes outputting such useful results possible. Using math and statistics in this way enables the algorithms to understand anything with a numerical basis. Decision trees are one of the most popular machine learning algorithms to date. The NumPy library provides essential functionality for scientific computing in Python. Matrices are useful for writing and executing programs in a speedy way because readers can use computer commands to execute matrix operations. The basic matrix operations are addition, subtraction, and scalar multiplication. The chapter explains the Bayesian point of view on probability. Operations on probabilities are indeed a bit different from numeric operations.