Increasing amounts of data

Our world is becoming more and more digital. In almost all areas of life - private and professional alike - data is collected: we accept cookies as we surf the internet, we make purchase using credit cards, we use navigation apps and spend time on social media. Machines send data about use, operating temperatures and output. Weather stations collect information on precipitation and wind speed.

Machine learning: systems that learn from data

Companies have been using data for a few years now, by using business intelligence software for data analysis and reporting. This data could be used even more effectively by using machine learning. Machine learning falls under artificial intelligence, and is an umbrella term for the artificial generation of knowledge from experience.

An artificial system - software - uses examples to learn rules that it can generalise after this learning phase is over. A machine learning model is able to compare apples to pears, for example: the system recognises patterns and regularities - in this case, in photographs of apples and pears. This learning data is then used by the machine learning model or algorithm to recognise whether a brand new image contains an apple or a pear. It then checks whether this recognition was correct or not. The quality of a machine learning model is measured by how high the error rate is. The more relevant data a machine learning model has available, so the more ‘practice’ it has, the better it learns from this data and the more precise the machine learning algorithm will become.

Artificial Intelligence

Machine Learning

Supervised and Unsupervised Learning

Supervised vs unsupervised Learning


In this example, machine learning takes place using verifiable data as supervised learning: the test data is given a correct label (apple or pear) by humans in advance. The algorithm can then compare its prognoses with the actual results and improve itself step by step.

This is different from machine learning using the unsupervised learning principle: no learning target is provided. The machine learning model is fed with data without any prior definition of what this data means. The machine learning algorithm recognises patterns and divides data up into clusters or categories without knowing what categories these are, i.e. under which labels they fall (such as ‘apple’ and ‘pear’).

The majority of machine learning processes used in practice entail supervised learning.

Supervised vs unsupervised Learning-Struktur


Deep Learning - what is that?

The so-called Deep Learning is a part of Machine Learning. This special method of information processing is based on neural networks. Thereby artificial intelligence is created by training methods. These methods are very similar to human learning. With the available information, the system can always link the learned information with new content and thus learn again. The machine is able to make decisions and forecasts and to question them again and again. This process then leads to a confirmation or a new start. As a rule, humans do not intervene in this learning process. Deep learning therefore teaches how to teach machines in practice and is particularly suitable for applications with large data sets from which patterns and models can be derived. The basis for this is formed by artificial neural networks, which are constantly being reconnected during the learning process.

Automated Machine Learning with DataRobot

Machine learning in practice

A whole range of technology that we use daily in our private lives is already based on machine learning algorithms. Examples are facial recognition in photos and videos, speech recognition on mobile phones, translation programmes online and computer spam filters.

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