Machine learning (ML)

Machine learning deals with the development of algorithms that can learn from data and improve without explicit programming.

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What is machine learning?

Machine learning can help companies and organizations make better decisions, optimize processes, and develop new products and services. However, it is important to use machine learning responsibly and ethically.

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The goal of machine learning

Recognize patterns and relationships thanks to ML

Machine learning algorithms can recognize patterns and relationships in data that are not obvious to humans. ML algorithms are able to identify complex patterns and relationships in data that are often hidden from the human eye. They achieve this by analyzing large amounts of data and applying statistical methods.

example: An ML algorithm can recognize recurring patterns in the purchase patterns of customers in the online shop, e.g. that customers who buy product A often also buy product B. This information can then be used to display personalized product recommendations and increase sales.

Make predictions using ML

Based on the patterns identified, ML models can make predictions about future events. These predictions can be made with varying degrees of accuracy and reliability, depending on the quality and quantity of the training data and the complexity of the model.

example: An ML model can make predictions about the course of a disease based on patient data and clinical patterns. This information can help doctors choose the right treatment and improve patients' quality of life.

Automate decisions with ML

Machine learning can be used to automate decisions, such as approving loan applications or identifying fraud attempts.

ML can be used to automate decisions in a wide range of areas. This results in greater efficiency, precision and cost savings.

example: An ML model can automatically review and evaluate loan applications by assessing the applicant's creditworthiness based on financial data and other information. This can speed up the process of granting loans and reduce the error rate.

Operating principle of machine learning

Machine learning algorithms learn from data that is provided in the form of training data sets. A training data set consists of sample data and the desired results (target variables). The algorithm analyses the training data and learns the underlying patterns and relationships. He then uses these findings to process new data and make predictions or decisions.

Different types of machine learning

There are different types of machine learning, which differ in how they work and their areas of application. The main types include:

Supervised learning

‍In supervised learning, the algorithm is equipped with both sample data as well as the desired results. The algorithm learns to predict the target variables from the input data. The goal of supervised learning is to teach an algorithm to learn a function that describes the relationship between input data (X) and Target variables (Y) depicts.

Unsupervised learning

In the case of unsupervised learning, the algorithm is only equipped with sample data before. The algorithm must structure the data itself and recognize patterns. In contrast to supervised learning, where both input data and the desired results (target variables) are available to the algorithm, in unsupervised learning, the algorithm only has the raw input data are available. His goal is to structure to recognize in this data and template to extract without getting explicit instructions.

Reinforcing learning

‍With reinforcement learning, the algorithm learns through trial and error. He receives rewards or penalties for his actions and adjusts his strategy so that he receives the maximum reward. In contrast to supervised and unsupervised learning, where the algorithm is given either the desired results (target variables) or the data structure, the RL agent receives Reinforcing learning no explicit instructions. Instead, he learns by trial and error in a dynamic environment.

Application areas of machine learning

Machine learning is used in a wide range of areas, including:

marketing: customer segmentation, customer behavior forecasting, campaign optimization
Sales: risk management, customer acquisition, cross-selling
Finances: fraud detection, credit assessment, stock market analysis
Medicine: disease diagnoses, treatment planning, drug development
Manufacture: Predictive maintenance, quality control, process optimization
Social Sciences: Behavioral research, opinion research, election forecasts

Ethical Aspects of Machine Learning

When using machine learning, ethical aspects such as data protection, data security and freedom from discrimination must be considered. It is important that data is used responsibly and that individuals' privacy is protected.

Comparing artificial intelligence and machine learning

Machine learning (ML) and Artificial intelligence (AI) are often used interchangeably, although they have important differences.

ML is a branch of AI that focuses on the development of algorithms concentrated from data learn and improve their performance without explicit programming.

AI is a general term that refers to the ability of machines to intelligent tasks Perform that are normally carried out by humans.

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Note: Our team benefited from the support of AI technologies while creating and maintaining this glossary.