In the current digital era, Artificial Intelligence is an accessible technology used in many fields. In order to take advantage of the advantages of AI, different techniques can be used. Among them, machine learning will make it possible to focus on machine learning algorithms to obtain relevant information from analyzed data. In many areas, the use of Machine Learning has a significant impact. Today, this technology appears to be an essential element for companies wishing to improve their customer knowledge and thus meet customer needs. Back on what is machine learning and what are its uses.
What is Machine Learning?
Also called “Machine learning”, machine learning is an area focusing on the learning capabilities of a machine and how it works. The objective of machine learning is therefore to train an algorithm to process the various data available in a relevant and efficient manner . Machine learning is divided into two main phases: learning and prediction.
Learning phase
The objective of machine learning is to let the machine build its reasoning system without having to impose a program beforehand. For this learning phase , the machine is based on several examples in order to understand the logic of the model that it must integrate. To learn, machine learning will focus on the masses of data to be analyzed in order to determine the transformation algorithm. From the examples studied, machine learning thus begins its learning phase independently.
Prediction phase
After integrating the reasoning and the algorithm of the problem in question, machine learning must be able to determine the purpose of a given situation . The more complete the learning of machine learning, the more precise the predictions obtained by this tool.
What are the techniques of Machine Learning?
The operation of machine learning is based on two types of techniques: supervised learning and unsupervised learning . These two techniques are part of the learning and prediction phases that characterize the operation of machine learning.
Supervised learning
This technique is based on a supervised learning algorithm, that is to say that the machine is interested in input data and responses to data already known. This processing of known data will allow the algorithm to form and develop a forecast model suitable for the data to be processed subsequently.
To develop these predictive models, supervised learning will notably be based on classification techniques. Putting classification techniques into practice implies that data can be identified and categorized according to their characteristics. The application of classification techniques can be found in different fields: digital, medical, banking. One of the most common examples of classification and automatic determination of the nature of an email: authentic or spam.
Supervised learning is also found in regression techniques . This technique is used to predict continuous variables. From the supervised learning of a variable data model, the machine is supposed to predict the different variations and fluctuations of real data such as temperature variations or energy demand according to the periods of the year.
Unsupervised learning
Unlike supervised learning, unsupervised learning will rely on input data whose answers are not identified. The objective of this type of technique is to highlight models intrinsic to the data processed. Among unsupervised learning, there is the clustering technique . This unsupervised learning model is the most common and makes it possible to identify common points between certain data and to group them clearly by group.
Concrete application cases of Machine Learning
On a daily basis, Artificial Intelligence is particularly present and is applied in different fields thanks to machine learning . Several cases allow us to illustrate the usefulness and the effects of this technology in our daily life.
Customer recommendations
When browsing the internet, data is collected about the user: purchase and search history. Based on this information, machine learning will be able to identify customer needs and which products can potentially interest each customer. Among the applications of this model, there is in particular the Netflix platform which offers recommendations to each user according to their viewing history and their tastes. The objective is to personalize the service offered according to the specificities of each client.
Medical diagnosis
It is undoubtedly one of the most interesting machine learning applications. By taking an interest in medical information, the machine learning model can establish a diagnosis of a patient's state of health and anticipate possible risks. In some cases, this diagnosis made through machine learning can prevent the occurrence of a heart attack.
Replacing images in videos
Thanks to an even more precise identification of images, certain machine learning techniques will make it possible to transform images directly into a video . This technology makes it possible to find similar elements in order to group them and to be able to modify them thanks to suitable software (example: PyTorch).
Multiple applications for machine learning
From fraud detection to voice recognition , including price forecasting, machine learning applications are multiple and can improve significantly different areas. Depending on the nature of the data, the mass to be processed and the use of the information obtained, the choice of applying such a type of algorithm may vary. Anyway, machine learning therefore has real potential and can allow many areas to improve.
