ML Fundamentals

A practical introduction to machine learning for engineers who build and operate systems. Twelve parts, from fundamentals through to production deployment.

5 parts

Part 1

What Is Machine Learning?

Mar 24, 2026 · 6 min read

Part 1 of the ML Fundamentals series. What machine learning actually is, the three main learning paradigms, and why it matters for infrastructure, automation, …

Part 2

Data Pre-processing and Evaluation

Mar 24, 2026 · 10 min read

Part 2 of the ML Fundamentals series. Cleaning messy data, selecting features, splitting datasets, and measuring whether your model is actually any good.

Part 3

Python ML Toolkit

Mar 24, 2026 · 14 min read

Part 3 of the ML Fundamentals series. Setting up a Python ML environment, and practical workflow patterns with Pandas, NumPy, Matplotlib, Scikit-learn, and …

Part 4

Classification — KNN, Naive Bayes, Decision Trees

Mar 24, 2026 · 15 min read

Part 4 of the ML Fundamentals series. Three foundational classification algorithms — how they work, when to use each, and hands-on implementation with …

Part 5

Regression and Decision Boundaries

Mar 24, 2026 · 17 min read

Part 5 of the ML Fundamentals series. Linear and polynomial regression, Ridge and Lasso regularisation, logistic regression, the Perceptron, and visualising …