There is no one Machine Learning algorithm. The program code looks different depending on the area of application and purpose. The same applies to the type of learning. Science roughly differentiates between these two forms of machine learning :
- Supervised Learning
- Unsupervised Learning
The different technology levels describe how much the teacher or the trainer of the algorithm has to intervene in the learning process – the model training. The algorithm receives a data set for training and a target with supervised learning, for example: “Is that a cat: yes/no?” The software can quickly come to the result due to specific specifications.
Supervised learning is divided into several subcategories: Semi-supervised learning, reinforcement learning, and active learning. In the last model, the algorithm asks for some of the answers; in the case of reinforcement learning, it gets better through a feedback and reward system.
With unsupervised learning, the algorithm receives a data set but no goals. In this case, he must find similarities and differences himself and assign them to clusters. In this way, for example, he independently distinguishes between dogs, cats, zebras, fish, and monkeys.
Goals: What Does Machine Learning Bring?
The importance of machine learning will increase significantly in the coming years since the algorithms can – and must – support us in various areas. Because of the rapidly advancing digitization, enormous amounts of data are generated.
Just think of the umpteen sensors built into cars to make autonomous vehicles possible. Intelligent programs are required to cope with this flood of information. Programs that filter and sort data on the one hand and derive decisions from them in milliseconds on the other.
Machine learning also helps processes become better and more efficient in other, less vital situations. For example, in e-commerce: Algorithms that know a customer’s behavior can recommend products that are more suitable for them.
In summary, machine learning has the following goals:
- Optimization of (business) processes
- Calculating Probabilities
- making predictions
- Recognition of relevant information or data
- Consolidation of pertinent information or data
Machine Learning: Examples
There are numerous areas in which machine learning is used. These include the following:
Weather Forecasts
With machine learning and deep learning, weather forecasts are becoming more precise. This is particularly important when it comes to forecasting storms or heat waves.
Medical diagnostics
The analysis of images is used in medicine, for example, to automatically detect skin cancer or a COVID-19 disease of the lungs. The “smart doctor” or the “smart doctor” supports real doctors in diagnosing diseases.
Customer Relationship Management
Modern CRM systems can automatically divide a large set of customer data into customer segments and find up and cross-selling potential for the respective customers.
Personalized Suggestions
Amazon recommends suitable products to its customers based on their purchase and search history. The situation is similar to the film suggestions at Netflix: Machine learning is used here to analyze customers’ consumption habits more precisely and recognize patterns.
Predictive Maintenance
When does the system filter need to be replaced? Could there soon be a standstill of the production plant due to an error? Predictive maintenance is about finding potential problems in advance, such as avoiding or shortening expensive repair work.
Crime-Fighting
Where could there be burglaries and similar crimes shortly? Machine learning can be used to calculate probabilities. The police can thus act proactively by reinforcing the emergency services in certain districts.
Internet Security
In hacking and other forms of cybercrime, there are increasingly perfidious methods of attacking IT systems, manipulating them, or stealing data. Machine learning helps find attacks and fraud methods (e.g., spam mails).
Traffic And Mobility
Machine learning algorithms help to improve traffic flow and road safety. For example, traffic jams can be avoided by switching traffic lights according to the situation. And the evaluation of the sensor data in connected cars ensures that there are fewer accidents.
Also Read: Artificial Intelligence In The Digital World