Machine Learning has become increasingly popular in recent years. But what is Machine Learning?
Machine Learning has been developing rapidly for a few years now. In the world of high-tech companies, everyone is constantly talking about Machine Learning and Deep Learning.
Machine Learning consists of creating algorithms capable of solving problems and making accurate predictions from a set of more or less complex data.
Machine Learning is an Artificial Intelligence technology that relies on the meteoric increase in data volumes (Big Data) as a result of the emergence of the Internet and the rapid development of databases.
In this article, we explain what Machine Learning is.
What is Machine Learning?
Machine Learning is a new branch of computer science that aims to make computers capable of learning from very large amounts of data. By accumulating the input and using mathematical and statistical tools to analyse it, the algorithms improve over time and eventually make the right decisions and predictions.
In other words, the machine uses historical input data to predict output values.
Eventually, machines are able to solve problems containing multiple parameters in order to predict future events.
For example, the manufacture of autonomous cars relies on this technology derived from Artificial Intelligence. By storing a huge amount of data that they capture via cameras, cars gradually learn to avoid obstacles, turn the wheel accurately and ultimately drive safely.
Among the sectors that use Machine Learning, we also find connected objects that learn to recognise people’s habits, recommendation engines such as Amazon, Facebook, YouTube or even anti-spam software, etc.
Different learning modes
There are two main types of learning: supervised and unsupervised. The use of one or the other depends on the type of data, its structure and what is expected from the machine.
- Supervised learning
Supervised learning is carried out in two stages. First, the data is catalogued and structured by the data analysts. During the first phase, the experts enter the labelled and already classified data into the machine according to a predetermined model. In this stage, the analysts literally train the machine by helping it to output suitable values based on the input data. During the second phase, the machine builds on this initial classification to arrange and order the new data by assigning appropriate labels.
This type of learning is required when, for example, we want to classify data into two classes (binary classification). But other tasks such as multiclass classification, regression modelling or even ensemble learning also make use of this model.
- Unsupervised learning
Also known as data clustering, this type of learning is required when there is no pre-established classification of the data. The computer must then determine for itself the structural logic behind the data. To do this, the machine cross-references and compares the data with each other to find certain similarities. This process enables the algorithm to categorise the data and classify it independently.
These learning algorithms are used to organise various data according to their similarity (clustering) or to identify irregularities within a data set, etc.
Applications of Machine Learning
There are many applications for Machine Learning. For example, in the field of health, doctors are beginning to use Artificial Intelligence to make diagnoses and propose appropriate treatments for their patients. Other sectors such as cybersecurity, finance and industry are increasingly using Machine Learning.
As you can see, Machine Learning has a bright future!
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