What is machine learning and how does it work?

What is machine learning and how does it work?


the machine learning plays an essential role in the analysis and processing of data. Closely related to Big Data, this form of artificial intelligence makes it possible to extract valuable data from massive and varied sources of information without the help of a human.

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What is machine learning?

Machine learning, or machine learning, is a modern science that helps discover patterns, i.e. repetitions, in one or more data sets. This data can be presented in different forms: words, figures, images, etc. Machine learning also makes it possible to provide predictions from this data based on:

  • statistics ;
  • data mining;
  • predictive analytics;
  • pattern recognition.

The definition of machine learning is still confusing for many people, despite the age of this concept. It was in the 1950s that the first machine learning algorithms were created. the Perceptron remains the best known of the algorithms created at the time.

To process a large panel of data in a limited time, computer systems rely on algorithms. The latter allow machines to learn from a determined base or not. Machine learning uses a wide variety of these algorithms:

  • linear or logistic regression algorithms;
  • decision trees;
  • the algorithms of clustering ;
  • the naive Bayesian classification algorithm;
  • association algorithms;
  • dimensional reduction algorithms;
  • neural networks.

The regression algorithms are used to understand relationships between data, whether dependent or independent. Depending on the data being compared, one speaks of linear regression, logistic or support vector machine algorithms.

The decision trees make it possible to establish rules based on classified data. They help to make a decision through questions whose different answers will lead to a final result.

The clustering algorithms consist of identifying groups of homogeneous objects and gathering data on the basis of similarity. Among the clustering algorithms that exist, we find in particular K-means.

I’Naive Bayes algorithm is based on probability. It provides the statistics of realization of an event based on previous data.

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The association algorithms are used to find links between data. They are also used to define association rules.

The dimensional reduction includes a set of techniques aimed at reducing the number of variables in the training data. It thus makes it possible to gain in efficiency in terms of results and analysis time.

The neural networks are deep learning methods. These are algorithms presented as a multi-layered network to identify specific features.

The three types of learning

Machine learning is based on the use of three machine learning techniques that vary depending on the type of algorithm used and the volume of data:

  • supervised learning;
  • unsupervised learning;
  • reinforcement learning.

I’supervised learning relies on a defined set of data. The data is labeled, which helps the machine learning model know what to look for in that data. The computer system thus trains itself to classify data on the basis of previously determined criteria. Examples of supervised learning algorithms include regression algorithms, classification algorithms, and support vector machines.

I’unsupervised learning, conversely, is to train a model on data that is not labeled. This means that the computer system will analyze the data without any indication and look for possible recurring patterns. The data is then classified according to the criteria that the system will have established itself. Unsupervised algorithms are clustering algorithms, association algorithms and dimension reduction algorithms.

In the case of thereinforcement learning, the algorithm will learn through training to arrive at a specific objective. To achieve this, he may try all sorts of different approaches. When he achieves his goal, the model is then rewarded.

What is the difference between machine learning and artificial intelligence?

Machine learning is a means and artificial intelligence a concept, each of which aims to improve the capabilities of computer systems. Although they are often discussed together, they do not have the same meaning and usefulness. This is why it is important to distinguish between these two notions.

I’artificial intelligence aims to give a computer system the ability to think and behave like a human. Artificial intelligence developers strive to create models that can mimic human behavior and can reason. As to machine learningit is a technique used to create and improve an artificial intelligence. Machine learning thus allows computers to learn and self-correct, but not to reason.

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Most of the progress made in artificial intelligence however depends directly on machine learning, but artificial intelligence does not rely solely on the use of machine learning. It also uses other methods such as simulation, digital twins or even expert systems. Machine learning is then sometimes defined as a sub-category of artificial intelligence.

How does machine learning work?

Machine learning gives computers the ability to learn based on models trained from streams of data to analyze. As learning progresses, machine learning algorithms improve their performance. The more data the models contain, the more accurate they are. After being trained, the models must know how to provide results from data that they have never processed.

Establish a machine learning model

To develop a machine learning model, four major main steps must be followed:

  • selecting and organizing a set of training data;
  • choosing an algorithm to run on the training data set;
  • training the algorithm;
  • the use and optimization of the model.

First, choose and organize a data set. This data will be used to feed the machine learning model so that it learns to solve the problem for which it was created. Data can be labeled or unlabeled. In both cases, the preparation and cleaning of the data must be the subject of particular attention, at the risk of biasing the training of the model and impacting the result of the predictions.

Next, one needs to select an algorithm to use on the extracted training data. The type of algorithm to run varies based on two criteria: the type and volume of training data and the type of problem to be solved.

The next step involves training the algorithm. The process is iterative. Through the algorithm, one executes variables, then compares the results with those that the algorithm should have generated. To improve the precision of the result, it is possible to adjust the variables before executing them again until the algorithm provides the expected result. Thus trained, the algorithm takes the form of the machine learning model.

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Finally, all that remains is to use the model and continue to improve it. The model is then used on new data, the source of which depends on the problem to be solved. The accuracy of the model can also change over time.

Examples of using machine learning

Machine learning is now proving its usefulness in a large number of companies in different fields. They have become aware of the competitive advantage provided by the ability to collect real-time data in order to develop predictions. Among the application sectors most concerned, we find in particular finance, governments, health, marketing, energy or transport.

Machine learning can indeed be used in different situations:

  • detection of fraud;
  • video games ;
  • automatic linguistic translation;
  • the conversion of spoken speech to the screen;
  • the automatic classification of images, in particular of medical X-rays;
  • analysis of transactional data;
  • analysis of data from CRM platforms.

Machine learning is also used to power many popular modern services:

  • the recommendation systems used by Netflix, YouTube, Spotify or Amazon among others;
  • search engines like Google and Baidu;
  • news feeds from social networks such as Facebook, Instagram or Twitter;
  • voice assistants like Google Assistant, Apple Siri or Amazon Alexa.

All these platforms collect large amounts of data about their users: the links they click on, the publications they interact with on social networks or the films and series they watch. It is then possible to use this data to feed machine learning algorithms and allow them to make predictions. In this way, the algorithms will be able to come up with better answers, search results or recommendations tailored to each user.

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