The role of statistics in machine learning: the basics for beginners

Why does machine learning need statistics?

Statistics is the foundation machine learning is built on. It explains how learning algorithms actually "learn" from data. The principles of statistical inference, for instance, are what let an algorithm pick up patterns and make predictions.

Statistical methods inside machine learning algorithms

Algorithms such as linear regression and logistic regression rest directly on statistical principles. They use parameter estimation methods — least squares, for example — to optimise their behaviour.

How do you get to grips with statistics without a deep maths background?

You can pick up the statistics behind machine learning starting from the basics: the mean, the median, the standard deviation. That gives beginners a gradual way in and a footing for the harder concepts later.

Statistical analysis in real machine learning projects

Take house price prediction as an example. Statistical methods are what tell you which factors — floor area, location, the age of the building — move the price the most.

In closing: why every developer should know some statistics

Statistics does more than sharpen your understanding of machine learning algorithms; it changes how you look at processing and analysing data generally. For a developer it is a key skill, and the route to building more accurate, more effective models.