Machine Learning
Subfield of artificial intelligence where algorithms learn from data and recognise patterns without being explicitly programmed.
Machine Learning (ML) is at the heart of modern artificial intelligence. From personalised product recommendations to automatic spam detection and predictive maintenance in industry: ML models permeate almost every area of the digital economy. Companies that adopt Machine Learning early gain significant competitive advantages through data-driven decisions.
What is Machine Learning?
Machine Learning is a branch of artificial intelligence in which algorithms independently recognise patterns in data and make predictions or decisions based on them. Unlike classical programming, where rules are defined manually, an ML model extracts rules automatically from training data. There are three main categories: supervised learning with labelled data, unsupervised learning for pattern recognition without labels, and reinforcement learning, where an agent learns optimal strategies through trial and error. Deep Learning with neural networks is a special case that achieves outstanding results especially with image, speech and text data.
How does Machine Learning work?
An ML project follows a defined workflow: First, data is collected, cleaned and split into training and test data. During training, the algorithm adjusts its internal parameters to minimise the error between prediction and actual outcome. After training, the model is evaluated with test data to detect overfitting. The model is then deployed to production, where it delivers predictions in real time. Monitoring and regular re-training ensure the model remains reliable as data changes.
Practical Examples
Bank fraud detection: ML models analyse transaction patterns in real time and flag suspicious bookings before damage occurs.
E-commerce product recommendations: Collaborative filtering algorithms analyse the purchasing behaviour of millions of users and suggest individual products.
Predictive maintenance in industry: Sensor data from machines is analysed by ML models to predict failures and plan maintenance proactively.
Chatbot training: NLP models learn from thousands of customer enquiries to answer standard questions automatically and relieve customer service.
Medical image analysis: Convolutional neural networks detect tumours in X-rays with accuracy matching or exceeding experienced radiologists.
Typical Use Cases
Churn prediction: Predicting which customers are likely to churn to take countermeasures in time
Quality control: Automatic detection of production defects from camera images on the assembly line
Price optimisation: Dynamic pricing based on demand, competition and customer segment
Document classification: Automatic categorisation and extraction of information from invoices, contracts and emails
Voice assistants: Recognition and processing of spoken language for Alexa, Siri and Google Assistant
Advantages and Disadvantages
Advantages
- Scalable pattern analysis: ML processes millions of data points that no human could evaluate manually
- Continuous improvement: Models become more accurate with new data
- Automation of complex decisions in real time without human intervention
- Competitive advantage through data-driven insights and personalised offers
- Versatile use from image and speech recognition to financial analysis
Disadvantages
- Data dependency: Without high-quality and sufficient training data, ML models deliver poor results
- Black-box problem: Complex models such as deep learning are hard to interpret and explain
- High initial effort for data preparation, feature engineering and model training
- Bias risk: Biases in training data lead to discriminatory decisions
Frequently Asked Questions about Machine Learning
What is the difference between Machine Learning and artificial intelligence?
How much data is needed for Machine Learning?
Can an SME use Machine Learning?
Related Terms
Want to use Machine Learning in your project?
We are happy to advise you on Machine Learning and find the optimal solution for your requirements. Benefit from our experience across over 200 projects.