What is machine learning?

Machine learning helps systems learn from data and improve without needing to be reprogrammed. It powers faster, smarter decision-making across modern enterprises.

What is machine learning (ML)?

Machine learning definition
How does ML work?
Types of machine learning
ML vs. AI vs. deep learning
Machine learning algorithms
Machine learning models
ML examples in industries
Machine learning use cases
ML risks and challenges
Machine learning FAQs

What is machine learning?

If AI is a brain, then machine learning (ML) is all the experiences and input that go into building knowledge. In the simplest terms, ML allows computers to not just manage data but also to learn from it. Instead of being told exactly what to do, these systems are trained to recognise patterns and make decisions on their own. This is what gives them the power to solve complex problems, adapt to new information, and get smarter with experience. From predictive maintenance to fraud detection and automated workflows, ML powers many of today’s most essential business functions. Unlike traditional programming, which follows fixed rules, it uses statistical models that evolve and grow with the more information they’re exposed to.

Machine learning definition

Machine learning is a type of artificial intelligence that allows systems to learn from data and make predictions or decisions without being explicitly programmed. Machine learning models are trained to identify patterns in examples they are given. Because they “learn” their accuracy and capabilities improve over time, supporting smarter automation, forecasting, and decision-making across business operations.

How does machine learning work?

The machine learning process starts with data and a goal – which could be something like predicting equipment failures, sorting images, or forecasting demand. Typically, a learning model uses one or more algorithms which train it to recognise patterns in historical examples. More complex models use a neural network that is made up of layers of artificial neurons that process information in stages.

Data preparation

Cleaning, organising, and labelling the data so it can be used for training. This includes standardising formats and filling in missing values. It also involves removing obsolete or irrelevant datasets and following set protocols to protect against unintentional biases or inaccuracies from creeping in.

Model selection

Choosing the right type of model for the task. For systems that use AI for a narrow set of tasks, simpler models will suffice. But increasingly, businesses run on connected cloud platforms that integrate disparate operations and datasets. This, of course, requires more sophisticated or hybrid models with greater flexibility.

Training

Feeding data into the model and reducing errors by adjusting internal parameters. For neural networks, this means passing data through multiple layers and gradually updating the weights using a method called backpropagation. With each cycle, the model gets better at mapping inputs to correct outputs.

Validation and testing

Evaluating how well the model performs on data it hasn’t seen before. This is where you determine that the model has actually learnt meaningful patterns. Testing ensures that it can generalise its knowledge to previously unseen real-world situations, not just parrot back memorised training data.

Deployment

Once a model is determined to be working well, it’s put into action. This might mean flagging anomalies, forecasting demand, or recommending next steps in a workflow. Today’s best systems continue to learn and adapt after deployment, using new data to refine performance over time.

What are the types of machine learning?

Several factors will determine the learning techniques used to train a machine. This can depend on the type of data available, and the intended goals and tasks. In many of today’s complex systems, various combinations of these learning methods are used:

1. Supervised learning

Supervised learning trains a model using labelled data. Each example in the dataset includes both the input (like an image or a data point) and the correct output (such as a category or a value). The model learns by comparing its guesses to the correct answers and adjusting itself to get closer each time. This is the most common type of machine learning. It is used in tasks like fraud detection, demand forecasting, and quality classification, where past outcomes are known and patterns can be learnt from historical data.

2. Unsupervised learning

Unsupervised learning works without labelled outcomes. The model explores the data on its own, looking for patterns, clusters, or structures that might not be obvious. Rather than trying to predict the right answer, it groups similar data points to reduce complexity in the dataset. Businesses use unsupervised learning to segment customers, detect unusual behaviour, or explore new market trends. The system can use its own learning to spot patterns that would be most relevant to the business.

3. Semi-supervised learning

As the name implies, semi-supervised learning is a hybrid model. It uses a small amount of labelled data along with a large amount of unlabelled data. This in effect “primes the pump” with labelled data so the system can get an early advantage. Semi-supervised learning is useful in fields like healthcare or industrial inspection, where data labeling is complex and sometimes subjective. By giving it a head start, this model becomes accurate at a quicker pace.

4. Reinforcement learning

Reinforcement learning is based on a trial-and-error approach. This means that the system interacts with its environment (such as a simulation) rather than simply learning from static data. It learns by getting feedback in the form of digital rewards or penalties. Reinforcement learning is often used in areas like robotics, supply chain optimisation, or systems that must constantly be adapting. In other words, it learns by doing, not just by ingesting data.

Machine learning vs. AI vs. deep learning

While machine learning drives many everyday applications, deep learning is used when the problem requires handling visual or unstructured data. Modern AI, as the broader category, typically includes both alongside other “smart” technologies such as computer vision and generative AI. Most of today’s sophisticated enterprise technologies use a blend of all three of these technologies.

Artificial intelligence

AI is the broadest category and refers to any system designed to mimic human intelligence. This includes reasoning, problem-solving, or interpreting language. Some AI systems follow pre-programmed rules, while others adapt solely through learning.

Machine learning

ML is a subset of AI focused on systems that learn from data instead of relying on fixed instructions. These systems build models based on examples and improve their performance over time. Most modern AI tools that adapt or improve use machine learning under the hood.

Deep learning

Deep learning is a type of machine learning that uses multi-layered neural networks to identify complex patterns in data. It excels at handling unstructured information like images, video, or natural language, and often powers tools like chatbots, image recognition, and speech analysis.

What are machine learning algorithms?

An algorithm is the specific method or process that every machine learning model uses to find patterns in data and to make predictions. Some models use only one algorithm whereas others use multiples or hybrids. The choice of which algorithm (or combination thereof) depends on the type of problem you solve, the nature of the data, and how much interpretability or accuracy is required. Here are a few of the most common ML algorithms and their uses:

Linear regression

A straightforward algorithm that is used to predict continuous values like sales revenue or energy usage. It analyses inputs and outcomes to deliver the most accurate reporting.

Example: Forecasting next month’s demand based on past sales and seasonality.

Decision trees

These algorithms follow an if-this-then-that directive, making their output easy to interpret and visualise. They’re good for tasks where transparency matters.

Example: Sorting incoming customer service tickets by issue type and urgency.

Random forests

The “forest” refers to a group of decision trees working together. By averaging the result of many trees with the same data, you reduce the risk of overfitting and inaccuracy.

Example: Detecting fraud by identifying unusual combinations of transaction behaviours.

K-nearest neighbours (KNN)

KNN compares new data points to known examples and classifies them based on similarity. It doesn’t require training in the traditional sense but is also slower with large datasets.

Example: Recommending similar products based on customer browsing history.

Support vector machines (SVMs)

These algorithms draw clear dividing lines between different groups of data, even when the data has many features. They work best when the groups are sufficiently different from each other.

Example: Flagging defective items based on sensor readings during production.

Naive Bayes

A probability algorithm that assumes each feature contributes independently to the outcome. Despite the “naïve” assumption that this is always true, it works very well in the right settings.

Example: Automatically sorting emails into spam and non-spam folders.

Clustering algorithms

Clustering algorithms are focused on grouping data points that are similar to one another without using labels. This makes them ideal for unsupervised learning models.

Example: Segmenting customers into behaviour-based groups for marketing campaigns.

Neural networks

While not strictly “algorithms” themselves, neural networks are model architectures that learn through repeated adjustments. They do, however, rely on algorithmic processes.

Example: Powering natural language live customer assistants

Machine learning models: How are they chosen?

Choosing the right algorithm and model requires matching the task to the problem. Teams consider what kind of data is available, whether the outcome is categorical or continuous, and how much explanation is needed for each prediction. Certain models are at their best with enormous data sets of similar types of information. Others excel at extrapolating from limited, complex examples. Businesses often choose multiple algorithms or use hybrid models to improve accuracy, coverage, or speed across different parts of the task or operation. To compare performance and fine-tune, data scientists often test several models side by side to find the best option for the business goal.

Machine learning examples in industries

Machine learning takes a different shape in every industry, but its value is consistent: helping businesses uncover patterns, anticipate outcomes, and respond faster. Here are a few examples of how it’s applied in specific sectors.

Distribution

Anticipating demand across complex inventories. Distributors must stay ahead of shifting orders, seasonality, and regional variation. ML supports better purchasing, inventory positioning, and replenishment strategies by refining forecasts and highlighting trends.

Fashion

Fine-tuning assortments and pricing. Fashion brands must track trends, analyse purchase patterns, and optimise size and colour assortments. ML helps tackle these needs as well as adjust pricing and promotions based on real-time behaviour and sell-through rates.

Food and beverage

Managing shelf life and spoilage risk. F&B companies can use ML to model temperature, humidity, or a variety of storage variables that affect freshness. This improves cold chain performance and helps reduce product loss from early spoilage or incorrect handling.

Healthcare

Prioritising high-risk patients for early outreach. In healthcare settings, ML tools can analyse patient histories, appointment patterns, and clinical indicators. This allows care teams to quickly flag risk, intervene earlier, and allocate resources where they’re most needed.

Manufacturing

Predicting quality issues before they escalate. Manufacturers produce voluminous IoT and production data which ML models can analyse in real time to detect early signs of defects, wear, or variability. This powers predictive maintenance, reduces waste, and improves first-pass yield.

Machine learning use cases

As every business leader knows, the capabilities and relevant uses of artificial intelligence and machine learning are evolving at an exponential pace. And while today’s best enterprise solutions all have a range of AI-powered features, it’s still very much a developing technology that will undoubtedly find ever-increasing operational applications. Here are a few of today’s most useful use cases:

Forecasting demand in dynamic environments
ML models can identify patterns across time, geography, or product lines to help teams anticipate demand more accurately. This improves planning, reduces waste, and aligns production or resource allocation to actual market behavior.
Detecting anomalies and reducing risk
Once it learns what “normal” looks like in a system, ML can learn to spot unusual activity in real time. The ability to predictively see irregularities, fraud signals, or potential breakdowns helps teams act faster and with more precision.
Optimising resource allocation
By weighing variables such as availability, cost, and lead time, ML can make predictions as to what materials, labour, or equipment will be needed and when. The benefit of this ability can be realised across the business – and in the bottom line.
Automating routine decision-making
Your team spends a lot of time determining priorities or deciding where to route issues or support tickets. ML can help streamline these complex decisions by accurately weighing and assessing volumes of relevant historical and current data.
Improving personalisation and recommendations
Whether suggesting products, services, or content, machine learning tailors experiences based on user behaviour. This leads to better engagement, higher satisfaction, and more relevant results across digital and customer-facing channels.

Machine learning risks and challenges

Machine learning imbues your systems with a previously unheard-of level of speed and capability. But as with any powerful tool, it functions best when you’re mindful of its limitations and treat it with care and respect.

Data quality and availability
Data is food for ML systems, and just like people, they suffer if they’re given too much junk. Before training commences, take care to ensure that data is complete, current, and as free as possible from inconsistency and bias. Building training guardrails should never be an afterthought.
Overfitting and generalisation
This flaw is typically well-intentioned. It often happens when an algorithm “rewards” the model for memorising the training data instead of actually learning the patterns. This can lead to failure when confronted with new inputs. This can happen with training sets that are too narrow or specific.
Interpretability and trust
A “black box” is a powerful deep learning model that begins to make decisions without offering clear reasons. In sectors like healthcare or finance, this lack of transparency can be disastrous. It’s essential to ensure that your teams are alert to black box issues and are regularly checking for them.
Maintenance and monitoring
A set-it-and-forget-it strategy does not work with machine learning. Even a well-designed model can degrade over time as conditions change – a phenomenon known as model drift. To remain sharp, ML systems need ongoing monitoring, retraining, and validation to remain accurate and useful. Integration and expertise Deploying ML is just the start. To be effective, it must also be smoothly integrated into workflows, systems, and business logic. Success depends on collaboration between technical teams and business stakeholders who understand how and why the model’s output will be used.

Conclusion

We call it machine learning technology but really, it’s a complex and flexible set of techniques and processes. From simple algorithms to sophisticated models, ML supports smarter decisions, faster workflows, and more adaptable operations. As its learning evolves, so do the opportunities for you to put it to work – making your business operations more efficient, agile, and competitive.

Discover how Infor’s AI and machine learning solutions integrate directly into your critical processes, helping you drive hyper-productivity across your organisation.

Explore Infor Enterprise AI

Machine learning FAQs

What is backpropagation in machine learning? 

What are digital “rewards” or “penalties” in reinforcement learning? 

Why do some models fail when exposed to new data?

Can machine learning models update themselves over time? 