Machine Learning: Which Approach Suits My Company? [5 Reading Tips]

In many areas of life, we encounter artificial intelligence (AI) almost every day. Over the last few years, its presence and impact on our daily lives has steadily increased – and there is no end in sight to this development. One branch of AI is called machine learning. It describes the artificial generation of knowledge from experience. This enables systems to build algorithms and recognise new connections. It is therefore logical that companies can benefit from this, especially in the digital age. In today’s reading tips of the week, we explain which disciplines exist within this branch of AI, what concrete advantages machine learning offers and how it differs from deep learning, another term in the buzzword jungle.

Machine Learning: Which Algorithmic Approach Is the Right One?

As is often the case, there are different approaches to machine learning as well. The first method is called supervised learning. Out of all machine learning methods, supervised learning is probably the easiest since the algorithm used already knows the results for a certain context. This prior knowledge enables systems to classify new data that has a certain similarity accordingly or to predict the results of comparable use cases.

The counterpart to supervised learning goes by the name of – surprise, surprise – unsupervised learning and is considered to be very challenging. This approach requires systems to recognise structures on its own and to classify new data based on them. A well-known example of this is the automatic facial recognition on Facebook. There is also a mixture of these two approaches, which – who would have thought it – is called semi-supervised learning.

The fourth and last machine learning approach goes by the name of reinforcement learning. Rewards and punishments are an essential part of this method. For correct behaviour, for example reaching a goal, systems are rewarded. As a result, algorithms learn how to react to which situation. This is where the most parallels to human learning can be drawn because systems internalise the feedback from their environment just like we humans do.

Machine Learning: Key Benefits & Use Cases

Of course, one would not go to the trouble of implementing machine learning in one’s own company if it did not result in considerable benefits. Machine learning can basically be used for two purposes: either to optimise processes or to drive innovation. It can effectively process large amounts of data and supports people in working faster and more efficiently as well as in discovering creative solutions. In addition, machine learning makes it possible for systems to take over complex tasks themselves. This has made the algorithms extremely valuable, especially with regard to monitoring.

Machine learning use cases can be found in a wide variety of fields. An example from everyday life that everyone probably knows are the recommendations on Amazon and Netflix. Machine learning has become indispensable for e-mail providers as well: they use the AI method to filter spam e-mails, thus protecting users from fraud. However, there are also use cases from industries where one would not have expected it at first glance. These include the medical sector, for example, where machine learning is used to detect cancer, but also the insurance industry, where risks can be better assessed with the help of machine learning.

Machine Learning Is Not the Same As Deep Learning

These two terms are often confused with one another. In fact, however, deep learning is only a subdiscipline of machine learning, and even though these two terms are often mentioned in the same breath, there is a significant difference: while humans intervene in the data analysis and learning process in machine learning, they do not get involved in deep learning. Accordingly, there is no manual intervention and the systems can gather new insights independently.

From a technical point of view, deep learning also involves neural networks. These networks serve as an abstract model of our brain and are used to process unstructured data and draw conclusions from it. Now it is not so difficult anymore to distinguish between the two terms, right?