Machine Learning (ML) is a branch of artificial intelligence that enables computer systems to automatically improve their performance on tasks by learning from data, without being explicitly programmed for each specific scenario. Instead of following fixed instructions, machine learning models identify patterns and make predictions or decisions based on data.
Machine learning underpins many modern technologies, from personalised recommendations and fraud detection to autonomous systems and predictive analytics. It has become essential in industries ranging from finance and healthcare to retail and autonomous systems, e.g. AP automation↗.
What is machine learning?
Machine learning is the practice of using algorithms to teach computers how to perform tasks by exposing them to data and letting the system learn patterns, instead of writing explicit step-by-step instructions.
At its core, machine learning revolves around three primary components:
- Data — structured or unstructured information used for learning
- Models — mathematical representations that learn from data
- Training — the process of tuning model parameters so that the system generalises from examples
Through repeated exposure to examples, a machine learning model “learns” how to produce useful outputs on new, unseen data.
How machine learning works
Machine learning involves feeding a model data, allowing it to recognise patterns and relationships, and then using that knowledge to make predictions or decisions. The overall process typically includes:
- Data collection: Gathering relevant data that represents the problem to solve.
- Data preparation: Cleaning and organising data, handling missing values, and formatting it for use.
- Model selection: Choosing an appropriate algorithm that can learn from the data.
- Training: Letting the model adjust its internal parameters by learning from the training data.
- Evaluation: Testing the model on new data to measure its performance.
- Deployment: Using the trained model to make predictions or decisions in real-world scenarios.
- Monitoring and iteration: Continuously evaluating and refining the model to maintain accuracy.
Modern machine learning systems often rely on large datasets and computational power, especially when using advanced techniques like deep learning.
Types of machine learning
Machine learning is commonly categorised into several types based on how models learn from data:
Supervised learning
In supervised learning, the model learns from labelled data, where each example has an associated correct answer (target). The model’s goal is to learn the mapping between inputs and outputs. Applications include spam detection, image classification, and credit scoring.
Unsupervised learning
Unsupervised learning involves learning patterns from data without labelled outcomes. Its goal is often to discover structure in data, such as grouping customers with similar behaviours (clustering) or reducing dimensionality.
Reinforcement learning
In reinforcement learning, the model (or agent) learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties. This approach is widely used in robotics, game playing, and autonomous navigation.
Semi-supervised and self-supervised learning
These approaches sit between supervised and unsupervised learning, utilising a mixture of labelled and unlabelled data or learning representations that benefit downstream tasks.
Machine learning vs. traditional programming
In traditional programming, developers write explicit instructions for computers to execute. Machine learning flips this paradigm: instead of writing rules, developers provide data and let the model infer the rules.
This difference matters because machine learning enables systems to adapt to complex patterns that are hard to express with fixed logic. For example, recognising cats in images is tedious to encode with explicit rules, but a machine learning model can learn this through examples.
Common machine learning algorithms
Numerous algorithms are used in machine learning, depending on the task and data type. Some widely used examples include:
- Linear Regression — predicts a continuous value
- Logistic Regression — predicts a binary outcome
- Decision Trees and Random Forests — tree-based predictive models
- Support Vector Machines (SVMs) — classification models
- K-means Clustering — unsupervised clustering
- Neural Networks and Deep Learning — layered models for complex pattern recognition
Each algorithm has strengths and limitations, and the choice depends on the problem, data size, and desired outcomes.
Machine learning in business and operations
Machine learning plays a transformative role in business by automating tasks, improving decision-making, and uncovering insights from data. In finance, ML is used for credit risk assessment, fraud detection, pricing optimisation, and algorithmic trading.
In customer operations, machine learning powers recommendation engines, personalised marketing, churn prediction, and customer segmentation. In supply chain and logistics, it helps optimise routes, forecast demand, and monitor equipment health.
Machine learning also accelerates internal workflows, many of which fall within the realm of pre-accounting↗. This includes document classification, invoice coding↗, processing, and anomaly detection, which are achieved by analysing patterns that would be difficult or inefficient for humans to catch manually.
Benefits of machine learning
Machine learning offers several key advantages:
- Scalability: Models can process large volumes of data quickly.
- Adaptability: Systems can improve as more data becomes available.
- Automation: Repetitive and pattern-based tasks can be automated.
- Predictive power: Models can forecast trends and outcomes.
- Personalisation: Experiences and services can be tailored to individuals.
These benefits make ML a valuable tool for deriving insights and automating complex decision processes.
Limitations and challenges
Despite its power, machine learning has limitations. Models are only as good as the data they learn from, meaning biased or poor-quality data can lead to inaccurate or unfair outcomes. ML models can also be opaque (“black boxes”), making interpretation and explainability challenging in certain use cases.
Machine learning requires careful data governance, ethical considerations, and ongoing evaluation to ensure responsible and reliable deployment.
Machine learning and AI
Machine learning is a subset of artificial intelligence. While AI refers broadly to any system that exhibits intelligent behaviour, machine learning specifically describes algorithms that learn from data. Most modern AI systems rely heavily on machine learning techniques, especially in areas like natural language processing↗, computer vision, and autonomous systems.
Machine learning vs generative AI
Machine learning enables systems to learn from data and make predictions or decisions. Most machine learning models analyse or classify information rather than create new content.
Generative AI↗ is a subset of machine learning that focuses on producing new outputs, such as text, images, or code, based on learned patterns. In short, machine learning powers prediction and analysis, while Generative AI specialises in content creation.
The Future of machine learning
Machine learning continues to evolve rapidly, driven by advances in models, data availability, and computational power. New research in areas like transfer learning, explainable AI, and efficient training techniques promises to expand what ML systems can achieve.
As organisations continue to embrace data-driven decision-making, machine learning will remain a key enabler of digital transformation and intelligent automation.
Summary
Machine learning is a foundational AI technology that enables systems to learn from data and improve over time without being explicitly programmed. By identifying patterns and making predictions, machine learning supports a wide range of applications across industries. Its ability to adapt, scale, and automate complex tasks makes it a central component of modern AI and data science strategies.