By Jonathan Harris, AI author and host of Turing’s Torch AI Weekly.
What this guide covers
Machine learning is not a single algorithm — it is a family of techniques that share one principle: learn from examples. You show the system labelled data, it finds patterns, and it uses those patterns to make predictions or decisions on new data it has never seen before.
The practical landscape breaks down into three broad types. Supervised learning trains on labelled examples — every input has a known correct output. Unsupervised learning looks for structure in data without pre-specified labels. Reinforcement learning trains an agent to take actions in an environment by rewarding good outcomes and penalising bad ones.
Each type suits different problems. Fraud detection is supervised. Customer segmentation is often unsupervised. Game-playing AI and robotic control use reinforcement learning. Most business applications are supervised or some mix of the two.
Where it works well
Machine learning works best when you have clean, representative data, a clearly defined prediction target, and a measurable way to evaluate whether the model is doing its job. Fraud detection, credit scoring, predictive maintenance, recommendation engines, and demand forecasting all fit this profile.
The interpretability advantage over deep learning is real in many regulated environments. A gradient-boosted decision tree or a logistic regression can be explained to a regulator, an auditor, or a sceptical executive in a way that a neural network with billions of parameters cannot. That matters in banking, insurance, and healthcare.
It is also cheaper and faster to train. Most machine learning models run on CPUs with modest cloud compute budgets. That makes iteration cycles short and experimentation cheap — a genuine advantage when you are trying to find out whether a problem is solvable before committing serious engineering effort.
Where it gets complicated
The biggest practical failure mode is not the algorithm — it is the data. Incomplete records, historical bias baked into labels, distribution shift between training data and live data, and inconsistent feature definitions across data sources cause most of the failures that get attributed to "AI not working". The model learns what it is shown. If what it was shown is not representative of what it will face, it will fail quietly and confidently.
Overfitting — learning the noise in training data rather than the signal — is a constant technical concern. Underfitting is less discussed but equally common in organisations that rush to deploy before proper validation. Both produce models that score well in development and disappoint in production.
There is also the governance problem. A model trained six months ago on data from a stable period may behave very differently after a market shock, a regulatory change, or a product redesign. ML models require monitoring, retraining schedules, and someone whose job it is to notice when performance drifts.
FAQ
Is machine learning the same as AI?
Machine learning is a subset of AI — the dominant one in current commercial applications. AI is the broader field including rule-based systems, optimisation, and planning. Most things called AI today use machine learning.
How is it different from deep learning?
Deep learning is a subset of machine learning that uses neural networks with many layers. It handles unstructured data like images, audio, and text very well. Standard machine learning typically needs structured, tabular data and is often more interpretable and cheaper to run.
Where is machine learning already used in business?
Fraud detection, credit scoring, demand forecasting, predictive maintenance, churn prediction, recommendation systems, and automated document classification. It is embedded in most enterprise software that does anything adaptive.
Why do machine learning projects fail?
Most failures trace back to data quality, poorly defined success criteria, training/serving distribution mismatch, or lack of ongoing monitoring. The algorithm is rarely the first thing to blame.