Machine Learning - 10 important Algorithms and their Application

Jan Schäfer

Machine learning (ML) is an artificial intelligence technology that learns patterns from data and makes predictions and decisions based on them - without being programmed to do so. It is therefore no wonder that companies and institutions from business, industry and research have been using machine learning for their processes for years.

The algorithms used play a decisive role in how machine learning works. We explain which machine learning methods are available, which algorithms play an important role and how companies can use them effectively.

The most Important in a Nutshell

  • Common machine learning methods are supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • The most important algorithms for machine learning include linear regression, decision trees and neural networks.
  • Important areas of application for machine learning algorithms include language processing, financial fraud detection and energy efficiency.
  • Konfuzio uses artificial intelligence and machine learning algorithms to optimize and automate companies' document processing. Let one of our experts advise you on the document workflow in your company now!
machine learning algorithms definition

What are Machine Learning Algorithms?

Machine learning is about teaching computers to recognize patterns in data and make predictions based on experience. Algorithms play a central role in machine learning. They enable computers to learn independently without having to be explicitly programmed to do so.

The basis of Machine Learning consists of data sets. Researchers, companies and public institutions use these data sets to train a machine learning algorithm.

During training, the algorithm learns from the data by identifying patterns and relationships between different aspects of the data, known as features.

One important method of machine learning is supervised learning. In this process, data scientists provide the algorithm with training data and the corresponding labels that represent the desired outputs. The algorithm then learns to predict these labels by identifying the relationship between the input data and the target results.

An alternative approach is unsupervised learning. Here, data analysts train the algorithm on data without providing specific labels. The algorithm attempts to find patterns or structures in the data on its own, without resorting to previously defined outputs. How these learning methods work in detail and what other approaches there are will be shown in the course of this article.

To implement machine learning methods, data managers can choose from various types of ML algorithms. The most important of these include decision trees, logistic regression and neural networks. Each of these algorithms has its specific areas of application. Together, they enable computers to learn from data and make intelligent decisions without users having to specify every detail in advance.

Methods of Machine Learning 

To solve problems with machine learning, data engineers choose from different methods. Depending on the method, different algorithms are then available to them. The following 4 machine learning methods are particularly common:

Supervised learning

In supervised learning, ML algorithms use known, i.e. labeled, data to identify patterns and correlations. Based on these findings, the algorithms then make predictions for unlabeled data.

Application in practice

Supervised learning enables precise modeling of correlations. It is particularly suitable for classification and regression tasks and is used in various areas such as speech recognition, Image recognition, financial forecasts, calculation of failure probabilities in production and the prediction of electricity consumption for a specific period.

Relevant machine learning algorithms

The algorithms relevant to this machine learning method include linear regression, random forest and decision trees. We will show how these work and what they are suitable for later in the article.

Unsupervised learning

With Unsupervised Learning, data scientists train the algorithm on data without specifying specific target values or labels. This means that the algorithm independently analyzes patterns and structures in the data without relying on predefined outputs. This enables it to discover hidden relationships and uncover previously unknown patterns.

Application in practice

Data engineers often use unsupervised learning to identify groupings (clustering) or to reduce dimensions, which allows them to process huge amounts of data more efficiently. The algorithms also help to create machine learning features.

Relevant machine learning algorithms

The K-Means Clustering, Principal Component Analysis (PCA) and Autoencoder algorithms, among others, are relevant for this machine learning process.

Semi-supervised learning

Semi-supervised learning is a type of machine learning in which the algorithm only partially works with labeled data during training. This means that data managers provide it with both labeled and unlabeled data. The algorithm uses the labeled data to learn patterns and then tries to apply these patterns to the unlabeled data.

Application in practice

This method is useful when marking large amounts of data is time-consuming or costly. It is mainly used in image and object recognition. Data managers first compile a small data set of known images. They then train a neural network for classification and apply it to the image data.

Relevant machine learning algorithms

Relevant algorithms for this machine learning method include Semi-Supervised Support Vector Machines (S3VM), Generative Adversarial Networks and Label Propagation.

Reinforcing learning

Reinforcement learning Reinforcement Learning a branch of machine learning, uses an algorithm to learn to make decisions by interacting with its environment. The algorithm is rewarded or punished based on the decisions made, which helps it to develop optimal strategies for action. 

In contrast to supervised, unsupervised and partially supervised learning, the algorithm does not require any sample data. Instead, it develops its own strategy in several iterative steps in a simulation environment.

Application in practice

Companies use reinforcement learning primarily in areas such as gaming strategies, robotics and autonomous systems where adaptive decision-making is required.

Relevant machine learning algorithms

The Policy Gradient Methods, Actor-Critic and Proximal Policy Optimization (PPO) algorithms, among others, are relevant for this machine learning process.

machine learning important algorithms

Machine learning - 10 important Algorithms

In practice, data scientists have numerous algorithms at their disposal for machine learning. These include the following 10:

Linear regression

Linear regression models the relationship between dependent and independent variables. It finds the best fit of a straight line to data points, making it useful for forecasting in areas such as finance and marketing. 

For example, companies use linear regression to model the relationship between advertising expenditure and sales figures. They forecast future sales based on historical data.

Logistic regression:

Logistic regression is suitable for classification tasks, such as predicting disease risks based on certain factors. It models the probability that an instance belongs to a certain class. 

In hospitals, for example, doctors use logistic regression to calculate the risk of a particular disease based on patient characteristics. This enables a more precise diagnosis and the identification of risk groups.

Decision trees

Decision trees make decisions based on features and divide the data set into subgroups. 

In the financial sector, for example, they help to assess credit risks. By analyzing customer characteristics, the bank can decide who to grant loans to and what conditions apply.

Random Forest

Random Forest is an ensemble technique that combines multiple decision trees. This improves prediction accuracy and is used in image recognition, medical diagnostics and large data sets. 

In practice, Random Forest is used, for example, to identify complex patterns in images. This is particularly helpful in medicine to diagnose diseases using imaging techniques.

Support Vector Machines (SVM)

SVM searches for the optimal dividing line between different classes in the feature space. This is useful in areas such as face recognition, text classification and financial forecasting. 

For example, companies in the financial sector use SVM to detect fraudulent transactions. It finds clear dividing lines between normal and suspicious transaction patterns.

k-Nearest Neighbors (k-NN)

The k-NN algorithm is a supervised learning method in which an instance is classified based on the majority of its k nearest neighbors. Proximity is often determined by the Euclidean distance, which measures the distance between points in space. 

In practice, for example, anomaly detection in network security uses k-NN to identify suspicious activity based on patterns in the vicinity of network traffic.

Neural networks

Neural networks consist of interconnected neurons and learn complex patterns. They are widely used in image recognition, speech processing and traffic. 

A practical example: In the automotive industry, neural networks are used in autonomous vehicles. They learn to understand complex traffic patterns and thus support the vehicle's adaptive decision-making.

Naive Bayes:

Naive Bayes is based on the Bayes' theorem and estimates the probability of a class under the assumption of feature independence. Applications include spam filters, text classification and medical diagnoses. 

A case from practice: In word processing, the Naive Bayes algorithm supports spam filters. It analyzes the probability that an email is spam based on certain words and filters them out.

K-Means clustering

K-Means groups similar data points into clusters by minimizing the mean distance. Applications range from customer segmentation to image compression and anomaly detection. 

In the retail sector, retailers use K-Means clustering for customer segmentation, for example. This enables them to develop targeted marketing strategies for different customer groups.

Principal Component Analysis (PCA)

PCA reduces the dimensionality of data by extracting the most important features. It is used in image compression, pattern recognition and visualization of high-dimensional data. 

In health research, for example, PCA is used to extract relevant features from extensive medical data. This facilitates pattern recognition in complex disease progressions.

machine learning algorithms use cases

5 important Use Cases of Machine Learning Algorithms

In practice, ML algorithms are used wherever companies need to process unstructured data volumes and gain insights from them. 5 important use cases that illustrate the functionality and significance of ML:

Financial fraud detection

In the financial sector, machine learning algorithms are used to detect fraud. They analyze transaction patterns, identity data and other factors to identify suspicious activity. This helps to minimize financial losses and ensure the security of transactions.

Personalized recommendation systems

Platforms such as Netflix and Amazon use machine learning algorithms to generate personalized recommendations for users. These algorithms analyze user behavior, take preferences into account and suggest products or content that match individual interests. This improves the user experience and increases customer satisfaction.

Voice processing and chatbots

Companies use a machine learning algorithm in speech processing to understand natural language. Chatbots use this technology to categorize user requests and respond appropriately. This optimizes customer service by automatically answering frequently asked questions and enabling interactive conversations.

Credit risk assessment in the banking sector

Banks use machine learning algorithms to assess the creditworthiness of customers. By analyzing financial histories, spending habits and other relevant data, the algorithms create risk profiles. This enables banks to make informed lending decisions and minimize credit risk.

Energy efficiency in the industry

Companies are using machine learning in industry to maximize energy efficiency. Sensors monitor energy consumption and production processes, while algorithms analyze patterns to identify optimization opportunities. This helps to reduce energy consumption, cut costs and promote environmental sustainability.

Use Machine Learning for your Document Processing - with Konfuzio

Konfuzio is a powerful software for the intelligent processing of documents. To this end, it has advanced algorithms for machine learning and Deep Learning,.

In practice, this means that with Konfuzio you are able to optimize and automate your entire document workflow.

To do this, the AI software automatically captures documents from various channels such as post and email and assigns them to created categories. It then recognizes all relevant data and extracts it via optical character recognition (OCR). The technology is also able to check the extracted information for accuracy and completeness. Finally, Konfuzio evaluates the collected data according to predefined criteria.

Do you still have questions about how you can optimize and automate document processing in your company with Konfuzio? Then talk to one of our experts now and let us advise you without obligation.

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