Machine Learning with Perceptrons: A Comprehensive Beginners Guide

Learn how to implement one of machine learning’s most influential algorithms

Photo by Timothy Chambers on Unsplash | Illustration Added

Here’s what you’ll learn in this article.

This article provides a comprehensive beginner’s guide to perceptron concepts and a walk-through of a basic implementation of a perceptron class in Python.

Perceptrons involve more moving pieces than familiar programming concepts like conditionals and loops. Thus, understanding them requires learning how those pieces fit together in a broader workflow. Because of the added complexity, perceptrons can be a challenging topic.

In this article, your understanding of perceptrons will build layer by layer, with more conceptual dots connecting as you move through the text. But before you begin that journey, here are some bullet points to serve as contextual grounding:

• Perceptrons classify between two classes of data.
• Classifications are made based on comparing feature values to a threshold.
• The comparison involves a calculation that uses special weight and bias term values.
• The weight and bias term values are adjusted during training to improve classification accuracy.