The contribution What is actual machine learning? Maria Gramsch first appeared on Basic Thinking. You always stay up to date with our newsletter.
Machine Learning is an essential sub -area of artificial intelligence. But what exactly is machine learning and how does this process work?
The Market volume of machine learning According to forecasts, the mark of 100 billion euros will be cracked this year. According to this, the market size should be around 103 billion euros this year. With a growth rate of around 35 percent annually, machine learning arrives in 2030 with a market volume of 460 billion euros.
The machine learning – or in German mechanical learning – has become an enormous economic factor as a sub -area of artificial intelligence. But what is machine learning and what is the importance in the AI area?
Artificial intelligence: What is machine learning?
Machine learning is a sub -area of artificial intelligence. Algorithms and models are used. These can recognize patterns and learn from experiences without being explicitly programmed.
Machine learning is particularly designed for the fact that algorithms and models find patterns in large data sets. The respective model is trained with example data.
Based on the experiences gained during this training session, a model can then predict and make a decision based on new or unknown data. Machine Learning’s goal is that AI is constantly improving and constantly expanding her knowledge.
The algorithms enable artificial intelligence not only to process data, but also to further learn by dealing with it. No special programming is required for this. Various algorithmic techniques can be based on machine learning.
Machine Learning: Which learning models are there?
Which of these models is used depends on various factors – for example on the data records fed in, but also on desired results.
At the monitoring Training a model with a data record in which there is already a correct answer (output or label) for each input (input). For example, the algorithm learns to differentiate between plants or sights. The goal is to develop a model that can classify new, previously unknown data as precisely as possible or predict values.
At the Unsignment learning If there are no already given answers, so the data are not labeled. The model only receives data in the input and must recognize patterns or relationships from them independently.
At the Learning partially monitored it is a mixture of the first two learning models. The model receives a small amount of data in which the answer is already known and a large amount of data in which this is not the case.
This learning model is often used because it is inexpensive and associated with less effort. Because the labeling of data – such as the pictures of plants – is very time -consuming.
In the fourth learning model, the Imprinance learningit is a special form of machine learning. A “learner” is used, this can be a robot, but also an algorithm. Through interaction with his surroundings, he tries out various things. For this he receives either rewards or punishments. The aim is to strengthen the knowledge of the learner through experience and rewards.
Also interesting:
- Wind turbines under water: Start-up presents new tide power plant
- Gafam, or: How Big Tech dominates the global economy
- Meta Ai: Everything you need to know at the start of Germany
- Ki-token: The smallest linguistic unity of artificial intelligence
The contribution What is actual machine learning? Maria Gramsch first appeared on Basic Thinking. Follow us too Google News and Flipboard.
Machine learning is a subset of artificial intelligence that involves the development of algorithms and models that allow computers to learn from and make predictions or decisions based on data. It is a powerful tool that has the potential to revolutionize many industries, including healthcare, finance, and transportation.
Actual machine learning involves training algorithms on large datasets to identify patterns and relationships within the data. This process allows the algorithm to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms can be used for a wide range of tasks, from image and speech recognition to natural language processing and predictive analytics.
As a tech industry expert, I believe that machine learning has the potential to greatly improve efficiency and accuracy in many areas of business and society. However, it is important to approach machine learning with caution and ensure that ethical considerations are taken into account, such as bias in data or algorithms.
Overall, machine learning is a powerful tool that can provide valuable insights and predictions, but it is important to understand its limitations and potential risks in order to use it effectively and responsibly.
Credits