Machine learning
Machine learning (ML) is a sub-field of artificial intelligence in which computer systems learn from data instead of being programmed explicitly for every task. An ML model recognises patterns in example data and uses them to make predictions or decisions for new, unknown data. The more relevant data is available, the more precise the results generally become.
Also known as: machine learning, learning systems, statistical learning
How does machine learning work?
At the centre of machine learning stands training a model on example data. An algorithm adjusts internal parameters until it represents the relationships in the training data as well as possible. The model can then carry the knowledge it has learned over to new data it never saw during training. That step is called generalisation and is a learning system's real value.
For a model to work reliably it typically goes through several phases: preparing the data, training, validation and finally production use. In every phase the quality and representativeness of the data play a decisive part. Faulty or one-sided data inevitably leads to faulty predictions, which is why careful data preparation often makes up the largest part of an ML project.
What types of machine learning are there?
There are three main categories. In supervised learning (Supervised learning) the model is given training data with known outcomes, for instance images with corresponding labels. It learns to map inputs to the right outputs and suits classification and regression.
In unsupervised learning (Unsupervised learning) there are no given answers. The model looks for structures on its own, for instance to form customer groups or spot anomalies. Reinforcement learning (Reinforcement learning) in turn learns by trial and error: an agent receives rewards for good decisions and improves its behaviour step by step. This method is often used in robotics and control tasks.
Machine learning vs. classic programming
With classic Software development developers define fixed rules by which a program processes input. This approach hits its limits, though, as soon as the relationships are too complex to capture fully in rules, for instance in image or speech recognition.
Machine learning reverses that principle: instead of prescribing rules, you give the system examples and let it derive the rules itself. Problems for which no clear logic exists can thus be solved. The price is a greater need for data and computing power and less traceable decisions.
Where is machine learning used?
Machine learning is now a fixed part of numerous digital products. Recommendation systems in online retail, spam filters, fraud detection in payments and content personalisation rest on ML methods. In industry too, machine learning supports predictive maintenance for instance, by forecasting failures early.
For companies of any size, machine learning offers concrete efficiency gains, through automated classification of documents, more precise sales forecasts or intelligent search for instance. What is decisive for success is less the choice of a particular algorithm than a clearly defined use case with measurable benefit.
Delivering machine learning with Elisabit
The road from an idea to a productive ML solution requires both technical know-how and a clear understanding of the business goals. Elisabit supports companies in identifying sensible machine learning use cases, building the data basis needed and integrating suitable solutions into existing processes.
Whether intelligent automation, data-driven forecasts or connecting modern AI models: we rely on practical solutions that deliver real value rather than technology for its own sake. Machine learning thus turns from an abstract buzzword into a concrete competitive advantage.
Frequently asked questions
What is the difference between machine learning and artificial intelligence?
Artificial intelligence is the umbrella term for systems that reproduce human-like cognitive abilities. Machine learning is a subfield of it and describes specifically the method of systems learning from data. All machine learning is AI, but not all AI necessarily uses machine learning.
How much data does machine learning need?
That depends heavily on the use case and the method chosen. Simple models sometimes get by with a few hundred examples, while complex tasks such as image recognition need very large amounts of data. More important than sheer quantity, though, are the quality and representativeness of the data.
Do you need special programming skills for machine learning?
Knowledge of languages such as Python and of statistics helps in developing your own models. Thanks to modern cloud services and pre-trained models, though, many ML features can now be used without deep specialist knowledge. For demanding projects, support from experienced professionals is still advisable.
Is machine learning the same as deep learning?
No. Deep learning is a specialised subfield of machine learning based on deep neural networks. Machine learning also covers many other methods, such as decision trees or linear models, which work without neural networks.
Related terms
Deep learning uses deep neural networks to recognise complex patterns in large volumes of data automatically.
A neural network is a computational model of connected neurons, modelled on the brain, that learns from data.
Generative AI independently creates new content — text, images, audio or code — based on patterns it has learned.
AI automation uses LLMs and agents to automate even unstructured business processes end to end.
Company-wide, productive use of AI with a focus on security, scalability and integration.
Put AI to work for your business?
We help you integrate artificial intelligence into your processes, your marketing and your website — strategically and securely.
Your contact
Stefan
I look forward to hearing about your project and finding the best solution together.
