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Machine Learning is on everyone's lips, because Big Data is becoming more and more relevant in today's society. Data feeds corresponding algorithms that learn from it and ultimately make decisions. Not to be confused with rule-based artificial intelligence (AI), in which humans fix rules for decisions in advance.
Table of contents
What is Machine Learning?
As a subfield of artificial intelligence, machine learning deals with the development of algorithms and models. These enable computers to learn automatically from large data sets (Big Data) and to make predictions or perform tasks - without being explicitly programmed by humans.
Machine learning focuses on discovering patterns and relationships in data to optimize decisions or actions. A simple example is an image recognition algorithm that differentiates between dogs and cats. Sample data is used to train the system, after which it can classify images of the animals into the appropriate category.
Machine Learning: What's behind it
While traditional software development focuses on programming fixed code, machine learning is based on independent learning from data. The algorithm learns from data and creates its own program code.
Machine learning can do a variety of tasks, such as:
The prediction of values based on the analyzed data
The calculation of probabilities for certain events
The detection of groups and clusters in a data set
The recognition of connections in sequences
The reduction of dimensions without much loss of information
The optimization of business processes
For machine learning to work and for the software to make decisions on its own, a human must train the algorithm. By providing training and sample data, the algorithm recognizes patterns as well as correlations and thus learns from the data sets. The model can then be applied to new data to make predictions or decisions. Data mining and predictive analytics ultimately use machine learning techniques.
In addition, Machine Learning is used in various fields such as image and speech recognition, medicine, financial industry and many others. In recent years, due to the availability of big data and advances in computer hardware and software, Machine Learning has gained a lot of importance and is expected to become even more important in the future.
The 3 methods of Machine Learning (ML)
There are three basic machine learning algorithms. These include Supervised Machine Learning, which learns from existing data and makes predictions from it. The second is Unsupervised Learning, which identifies patterns within data sets, and the last is Reinforcement Learning, in which an optimal solution path is learned by the algorithm.
1. Supervised Machine Learning
In supervised learning, an algorithm is trained with data that is already labeled with the correct answers or labels. The model learns to recognize patterns or relationships in the data and can then be applied to new, unlabeled data to make predictions or decisions. Application examples include face recognition or sales forecasting.
2. Unsupervised Machine Learning
In unsupervised learning, there are no predetermined labels or answers. The model learns to recognize patterns or relationships in the data and can then use this information to form groups and clusters or identify relationships. This is useful for making product recommendations to customers or performing customer segmentation.
3. Reinforcement Learning
In reinforcement or reinforcement learning, the algorithm interacts with its environment and learns independently with the help of a reward system. It makes decisions and then receives feedback from the user about whether these decisions were right or wrong. It then adjusts its strategy to achieve better results.
Application areas of Machine Learning
Machine learning is used in many fields. The different types even in different subareas:
Supervised learning, for example, is mainly used for classification and forecasting. In weather forecasting, sales forecasting or power consumption forecasting. It is also used for classification, text recognition or predictive targeting.
Unsupervised learning is used in cluster analysis or dimension reduction. For example, to detect structures, for market segmentation, customer segmentation or Big Data visualization.
Reinforcement learning is mainly used for personalization and advertising. This includes, for example, autonomous driving, traffic control, robotics, and gaming AIs.
The process of machine learning
A practical machine learning process is composed of the following sub-steps. This process is run through as often as necessary until the desired results are achieved:
Problem definition, goal definition and knowledge exchange:
The goals and purposes of machine learning must be clearly defined in advance. It is important to define the goal to be optimized and to exchange the required knowledge.
Data sourcing, transformation and feature extraction:
This step is usually the most time-consuming, as high-quality data is crucial. This is where an ML feature store can create high efficiency.
Learning phase
: In this step, the actual machine learning takes place and the algorithm is trained.
Interpretation of results
: Interpretation of results and models is essential to the process. This is an important step to also create acceptance for machine learning in a department. After all, humans want to understand what the algorithm is doing.
Productive use:
Developing machine learning in innovation labs alone and not using it in real processes does not offer any added value. The technical requirements for machine learning are complex, so going live is not always straightforward.
The most important roles in the ML process
An optimized machine learning process requires a large number of employees. These take on specific roles and areas of responsibility:
Roll | Tasks |
Business Stakeholder | Engage in customer-centric work and incorporate their needs and experiential values into the process |
Data Analyst | Perform the pre-analysis, feature engineering or visualization of the results → Data preparation. |
Data Engineer | Check the connection of data sources, manage the infrastructure and provide data → one of the most important roles |
Data Scientist | Define use cases and the appropriate algorithms, also prepare data and optimize the entire process → Key players |
ML Engineer / Specialist | Implement models, further develop methods of ML |
Data Translator / Data Ambassador | Translation of applications and results as well as information transfer towards the domain |
Difference from related terms
Confusion often arises about how machine learning fits into the big picture and how to distinguish it from other related terms.
Data Analytics: limited to statistical analysis of data, but boundaries are becoming blurred as upfront ML analysis is essential.
Data Science: Machine learning is a component of Data Science.
AI: AI includes all algorithmic functions that imitate human behavior. ML is also a subset of this.
Neural networks: They are used for a variety of use cases in ML. These include, for example, classifications or predictions. Accordingly, they represent a manifestation of ML.
Deep Learning: Is a type of neural network that has multiple layers. Accordingly, it can be called implementation.
Big Data: Big data offers ML the possibility to draw better conclusions than from smaller data sets. Although ML also works with a small amount of data, the variety of data provides a qualitative increase in the process.
Data mining: Here, patterns in data are examined, whereby data mining also uses methods of ML. Nevertheless, both approaches are not equivalent.
Prescriptive Analytics: This method describes the part of machine learning that is responsible for prediction. Through various predictions, an action is recommended.
Potential risks & challenges in ML
Machine learning offers a variety of benefits and practical application areas, but there are also downsides. Above all, the danger that an algorithm does not reflect reality or that excessive reliance is placed on results is often criticized.
Incorrect or faulty data, for example, lead to the algorithm spitting out faulty results at the end. Especially if no user-centered research is conducted before the start or the data is not reliably transformed, errors will occur in the end.
Another risk is a bias due to the data itself: Very concise patterns from the past can have a negative impact on the algorithm. In addition, ML algorithms often have an opaque way of working. Especially because there are so many parameters and partial results, this often raises trust issues with humans.
While there are some dangers and challenges to working with ML, it offers many technical advantages for optimizing processes and will thus continue to be an important part of understanding Big Data in the future.
Machine Learning in ecommerce
Machine learning in ecommerce offers numerous opportunities to improve the customer experience and optimize business processes.
Personalized shopping experiences
An important application of Machine Learning in ecommerce is the personalization of the shopping experience. By analyzing customers' behavior on the website or app, such as the products they view or purchase, Machine Learning can provide customers with personalized product recommendations. This helps guide customers more quickly and easily to the products they are looking for or are likely to be interested in, which in turn can lead to higher sales.
Predictions in ecommerce
Another important application area is the prediction of product demand and inventory management. Machine learning enables ecommerce companies to make predictions about which products will be in high demand in the future and adjust their inventory management accordingly. This allows them to ensure they have enough stock to meet demand while avoiding holding too much inventory.
Fraud prevention
Another important application of machine learning in ecommerce is fraud prevention. Ecommerce businesses need to guard against fraud attempts to provide a safe and reliable shopping experience for their customers. Here, Machine Learning can help detect and block suspicious activities such as unusual orders or transactions before they do the damage.
Machine Learning for more efficiency
In summary, Machine Learning plays an important role in ecommerce to increase efficiency, improve customer loyalty and drive business growth. However, implementing Machine Learning technologies requires thorough data analysis and customization to meet the specific needs of each ecommerce business.
Frequently asked questions
What is Machine Learning?
Machine learning (ML) is a field of artificial intelligence that allows computers to learn automatically from experience without being explicitly programmed.
What are the different types of machine learning?
There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning.
What are the applications for machine learning?
Machine learning can be used in many applications, including image and speech recognition, text analytics, recommendation systems, predictive maintenance, cybersecurity, and others.
