Machine Learning
Learning from data
Machine learning is a branch of artificial intelligence in which software learns from patterns in data instead of executing hard-coded rules. A model is given examples, finds relationships in them, and then uses those relationships to make predictions or decisions about new data.
How machine learning works
In a classic program, a developer writes exactly what the software should do in every situation. Machine learning works differently: the model is given many examples along with the correct answers, and during training it adjusts its own internal parameters so that it responds to similar cases as accurately as possible.
There are several basic types. In supervised learning, each example has a correct result attached, such as an email labeled as spam or not. In unsupervised learning, the model finds hidden groups and patterns in the data on its own. The quality of the result always depends on the quality and amount of data.
Where machine learning is used
Machine learning powers many features we now take for granted. In a business it fits anywhere there is enough historical data and you want to predict future behavior or automatically classify new cases.
- Forecasting demand and planning inventory.
- Detecting suspicious transactions and fraud.
- Recommending products based on customer behavior.
- Classifying and tagging documents, emails, or images.
Relationship to AI and LLMs
Machine learning is at the core of modern artificial intelligence. Even large language models like LLMs are built on machine learning; they simply work with vast amounts of text and a specialized architecture. For business use it is important to know that a model is never finished once and for all: its performance has to be monitored and tuned over time as new data comes in.
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Explore custom developmentFrequently asked questions
What is the difference between machine learning and artificial intelligence?
Artificial intelligence is the broader term for software that solves tasks requiring intelligence. Machine learning is a specific approach to achieving that ability, namely learning from patterns in data. Most of today’s AI is built on machine learning.
How much data do I need for it to work?
It depends on the task. Some simpler models do fine with a small amount of data, while more demanding tasks need significantly more. More important than the sheer amount is the quality and relevance of the data to what you want to predict.
Can the model be trusted one hundred percent?
No. A model makes predictions with a degree of uncertainty and can be wrong, especially on cases that differ from its training data. That is why important decisions should combine its output with human review.