Artificial Intelligence is transforming the modern world - but beneath the headlines about robots, automation, and smart machines lies something much simpler: the algorithm. These sets of rules, calculations, and logical steps are the beating heart of every intelligent system. In The Basics of AI Algorithms, Julian Vexley breaks down this vast and technical subject into ten clear, accessible chapters designed for the curious reader.
This book explores how machines learn, adapt, and evolve through data. It starts with the core question - what exactly is an algorithm? - and then guides you through the major branches of machine learning: supervised learning, unsupervised learning, and reinforcement learning. From there, it examines the models that make AI work, including neural networks that mimic the brain, decision trees that provide clarity and structure, and ensemble methods that show the power of collaboration between multiple algorithms.
Each chapter reveals one of the essential pillars of artificial intelligence, explaining complex concepts in plain English without formulas or jargon. You'll learn how Support Vector Machines find the best dividing line between data points, how K-Nearest Neighbours classify information by proximity, and how Gradient Descent helps machines improve themselves step by step - the same process that powers modern deep learning.
Throughout the book, Vexley combines insight, historical context, and practical examples to show how algorithms shape the world around us - from search engines and medical diagnostics to voice recognition and autonomous vehicles. The writing is clear, engaging, and ideal for readers who want to understand AI without getting lost in mathematics or code.
More than a technical primer, The Basics of AI Algorithms is a story about how logic became intelligence. It explains how centuries-old ideas from mathematics and philosophy have evolved into digital systems capable of analysing vast amounts of information and making decisions that impact millions of lives. It also explores the human side of AI: how data quality, ethical responsibility, and human oversight remain vital in a world increasingly governed by automation.
By the end of this book, readers will not only understand how AI algorithms work but also why they matter - and how they continue to redefine creativity, problem-solving, and the nature of intelligence itself.
Whether you're a student, professional, or simply fascinated by the technologies shaping the future, The Basics of AI Algorithms offers a clear, comprehensive, and thought-provoking introduction to the logic behind the machine.
Chapters include:
01. The Core Concept: What Is an Algorithm in AI?
02. Supervised Learning: Learning from Labelled Examples
03. Unsupervised Learning: Discovering Hidden Patterns
04. Reinforcement Learning: Learning Through Trial and Error
05. Neural Networks: Mimicking the Human Brain
06. Decision Trees: Transparent and Interpretable Logic
07. Support Vector Machines: Finding the Best Separating Line
08. K-Nearest Neighbours: Learning by Proximity
09. Ensemble Methods: The Power of Collaboration
10. Gradient Descent: The Path to Optimisation
The Basics of AI Algorithms is your essential guide to understanding how machines think - and how their logic is shaping the world of tomorrow.
