Now that we understand what machine learning is and how it works, let's look at some practical examples. There are many applications, some of which have entered our daily lives without us even realizing it. Let's think, for example, of search engines : through one or more keywords, they return lists of results that are the effect of algorithms with unsupervised learning.
Another common example is related to anti-spam email filters based on machine learning systems that learn to intercept suspicious or fraudulent messages and act accordingly (for example, by deleting them before they are delivered). Systems of this type are also used in the financial sector for fraud prevention.
Interesting examples of machine learning with supervised learning come from the scientific research sector in the medical field. Algorithms learn to make increasingly accurate predictions to prevent the outbreak of epidemics or make diagnoses.
Systems based on reinforcement learning are, on the other hand, the basis for the development of autonomous vehicles which, thanks to machine learning, learn to recognise the environment around them and adapt their 'behaviour' based on the specific situations they have to face.
So-called recommendation systems also exploit machine cambodia whatsapp data learning by learning from the behaviour and preferences of users browsing websites, platforms or mobile applications: from Amazon to Netflix or Spotify. Finally, machine learning plays a central role in Industry 4.0 thanks to its ability to recognise patterns in data and make autonomous decisions or provide useful information for human-driven decisions.
What is machine learning used for?
As we have seen, machine learning is used in many fields , from scientific research and medicine to cybersecurity and industry. Wherever a machine can learn from data and experience, there is room for machine learning applications, regardless of the target sector.
Considering the business world as a whole, machine learning is a valid aid to transform data into value, but on its own it has no miracle-working qualities. ML must be inserted into a platform that centralises data science activities and turns it into a collaborative discipline. If this happens, the collection of process data allows for obtaining real-time information and deepening the understanding and improvement of processes.
Normally, the development of machine learning systems is the task of a professional profile called a machine learning engineer or, alternatively, a data scientist. What these two figures have in common is data analysis and, beyond corporate functions, it is not uncommon for them to work together. The best training option for those who want to work in this constantly growing field is to complete their university education with a Master's Degree in Artificial Intelligence & Machine Learning for Business or a Master's Degree in Big Data & Analytics .