What is machine learning?
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on the development of algorithms that improve through experience and data. ML enables computers to learn from data and make decisions without explicit programming.
Essentially, it is about creating algorithms that optimize their performance over time by processing more data. Unlike traditional programming, where computers follow predefined instructions, an ML system learns from examples and tasks, recognizing patterns and features on its own.
For example, if you want a computer to recognize pictures of cats, you don’t give it specific instructions on what a cat looks like. Instead, you show it thousands of pictures of cats and let the machine learning algorithm figure out the common patterns and features that define a cat. Over time, as the algorithm processes more data, it gets better at recognizing cats, even when presented with images it has never seen before.
The ability of machine learning to learn from data and continuously improve is driving numerous technological advances in various industries, from finance to healthcare.
Definitions around machine learning
Machine learning, artificial intelligence and deep learning are often confused with each other, but their differences are crucial:
- AI, as an umbrella term, deals with the development of intelligent behavior through algorithms that mimic human intelligence. This includes learning, logical thinking and self-correction.
- Machine learning is a sub-area of artificial intelligence that uses algorithms to learn from data and make predictions. This can be done, for example, through supervised or unsupervised learning, whereby models can predict numerical values or group data points based on pattern recognition.
- Deep learning, in turn, is a specific branch of machine learning that relies on multilayer artificial neural networks (ANN) inspired by the structure of the human brain. Compared to conventional ML algorithms, deep learning algorithms are more complex, more hierarchical and can achieve extremely precise results from large amounts of data.
How does machine learning work? A look at the 7 steps of the process
The way machine learning works can be broken down into a process of seven steps. Here we take a detailed look at each of these steps:
1. data collection
The first step is to collect data – the basic element of machine learning. The quality and quantity of data have a direct impact on the performance of the model. Data can come from a variety of sources, from databases to text and image files to web scraping.
2. data preprocessing
Data pre-processing (data preparation) is crucial. It includes cleaning the data (removing duplicates, correcting errors), dealing with missing data (removing or filling in) and normalizing the data (scaling to a standard format). These steps improve data quality and ensure correct interpretation by the machine learning model.
3. choosing the right model
Data preparation is followed by the selection of a suitable machine learning model. The choice depends on the nature of the data and the problem to be solved. Different models such as linear regression, decision trees or neural networks are available, depending on the specific requirements.
4. model training
Once the model has been selected, training takes place. This step involves feeding the prepared data into the model and allowing it to adjust its internal parameters to better predict the output. It is important to avoid overfitting and underfitting to ensure optimal model performance.
5.model evaluation
After training, it is crucial to evaluate the model performance before it goes into use. This involves testing the model with new data not seen during training. Evaluation metrics such as accuracy, precision and recall are used to analyze performance and ensure the model is working effectively.
6. hyperparameter adjustment and optimization
After model evaluation, it may be necessary to adjust the hyperparameters to improve performance. This process, known as hyperparameter tuning or optimization, can include techniques such as grid search or cross-validation. The aim is to optimally adapt the model to the specific requirements.
7. forecasts and provision
After successful training and optimization, the model is ready to make predictions for new data. This step involves feeding new data into the model and using the model output for decision-making or further analysis. Deployment of the model takes place in a production environment where it can process real-time data and provide real insights, a process often referred to as MLOps.
Types of machine learning algorithms
Supervised learning, also known as supervised machine learning, uses known data to identify patterns and correlations. Here, the algorithm uses a training dataset containing sample data to learn the patterns. In the context of supervised learning, training always takes place in connection with a target variable that the algorithm is supposed to predict correctly. This target variable can either be a class (e.g. termination yes/no) or a numerical value (e.g. turnover for the next month).
This machine learning approach is particularly relevant for companies that want to make well-founded predictions. By using known data, the algorithm can learn to recognize patterns and use them to make accurate predictions about the target variable. This process is an essential part of the development of artificial intelligence (AI), especially in the context of big data and the application of advanced algorithms such as deep learning.
The fundamental difference to supervised learning is that unsupervised machine learning is not designed to make predictions for a known target variable (e.g. classification or forecast). In this case, the algorithm does not receive any sample data. Instead, it should independently discover structured groups and patterns in the data.
The results of unsupervised learning must be evaluated by data scientists using relatively “soft” factors, as there are no predefined target variables. Adapting the results to the business application requires a deep understanding of the data and business objectives.
This machine learning approach offers companies the opportunity to identify hidden correlations and structured groups in their data, which can provide valuable insights for strategic decisions. It is an essential aspect of the development of artificial intelligence (AI) and plays a significant role in the processing of big data.
Reinforcement learning, also known as reinforcement learning, represents a special form of machine learning. In this approach, the algorithms actively interact with the environment and are evaluated by a cost function or a reward system. This enables them to independently learn strategies for solving problems and maximize the reward.
The key difference between supervised and unsupervised learning is that reinforcement learning does not require example data provided in advance to show it which action or action is correct in certain situations. The algorithm can develop its own strategy in many iterative steps in a simulation environment. The evaluation is carried out using a cost function that provides positive or negative feedback in the form of rewards or punishments. Based on this feedback, the system autonomously develops a strategy to maximize the reward function.
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Application areas of machine learning
Machine learning (ML) has permeated various industries in recent years and is having a decisive impact on the way companies optimize their processes and implement innovative solutions. From the logistics sector to the manufacturing industry and the field of mechanical engineering, there are numerous fields of application in which ML is having a transformative effect.
In logistics, for example, machine learning enables precise route optimization, warehouse management and freight forecasting. In the manufacturing industry, ML is revolutionizing production processes, and in mechanical engineering, the development of intelligent, self-learning systems.
The future of these industries with ML is promising. Advances in deep learning and other ML technologies will lead to even more precise analyses and well-founded decisions. The increasing networking of devices on the Internet of Things (IoT) will further increase data availability, which will further boost the performance of ML algorithms.
In the healthcare sector, machine learning (ML) plays a central role in the precise analysis of medical data. The continuous development of ML algorithms enables companies in the healthcare sector not only to identify patterns in large amounts of data, but also to drive intelligent solutions for diagnosis and therapy individualization.
In the complex world of law and administration, also known as legaltech, machine learning is revolutionizing the processing of legal documents and contracts. These advanced algorithms help companies to design more efficient processes and maintain the integrity of legal procedures.
In the field of forward-looking logistics, known as predictive logistics, companies are relying on the power of machine learning and big data. The continuous analysis of large amounts of data enables precise predictions of resource requirements and stock levels, which leads to more efficient logistics management.
The transportation and mobility industry benefits significantly from machine learning, especially in the area of intelligent traffic control. By analyzing traffic data and applying advanced algorithms, ML helps not only to improve traffic flows but also to drive the development of autonomous vehicles.
In the dynamic environment of commerce, marketing and sales, companies use machine learning to refine their strategies. Personalized recommendations and the analysis of customer behaviour by ML algorithms lead to a precise adaptation of sales strategies and increase the success of marketing campaigns.
Machine learning plays a key role in proactive IT security. Through the continuous development of security algorithms, companies can detect and defend against threats at an early stage by analyzing network activities and identifying patterns of potential security risks.
The evolution of digital assistants is largely driven by machine learning. Through the use of deep learning algorithms, these assistants continuously adapt to individual needs, enabling more natural human-technology interaction.
Predictive maintenance uses machine learning to plan preventive maintenance measures. Analyzing machine data enables companies to predict failures and carry out targeted maintenance work, which leads to increased plant availability and a reduction in operating costs.
Machine learning plays an indispensable role in these diverse areas of application by enabling companies to gain insights from large amounts of data, act more intelligently and drive innovation in the age of artificial intelligence and big data.
Machine learning as part of IDP at ExB
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With machine learning, our IDP solution provides a powerful platform to automate your document processes. From data extraction of relevant information to intelligent categorization of your documents, our product not only optimizes your efficiency, but also enables you to achieve more accurate and precise results.
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