deepl learning definition

Machine learning Definition

Rest assured, Machine Learning is not vocabulary straight out of a science fiction film. Machine learning refers to computer programs that are useful to us every day. This article brings you the definition, how it works, but also different concrete examples.

Machine Learning is automatic learning

A branch of artificial intelligence, Machine Learning is a scientific field based on computer science. Machine learning is based on different types of algorithms with their own workings. That said, the majority of these programs aim to discover “patterns”. These are recurring patterns or patterns in a database. The computer then retains logic from a compilation of statistics, words or images.

Machine Learning programs thus draw on databases, regardless of the file format. The software assigns patterns or patterns that recur often enough to become established rules. Knowledge of these models is used to improve the execution of a task. In other words, the program becomes more efficient when it better understands what is happening by dissecting the data.

Using numbers or simple image sequences, Machine Learning algorithms gain autonomy. These bases also allow them to make predictions and anticipate the decisions to be made. This automation relies on a lot of programming. Indeed, applications represent only the tip of the iceberg. The other side includes hundreds of hours of coding.

Examples of concrete applications

Before continuing, it should be noted that Artificial Intelligence is based on Machine Learning. This category constitutes one of its pillars. In any case, large groups operating in Web 2.0 can hardly do without it. These multinationals use it to recommend products to their customers, adapt features or pay for their services.

  • YouTube offers videos to visitors using Machine Learning. Content is displayed based on details such as geographic location, the language of the program used or the subscriber profile. Netflix and Spotify use the same system, but with algorithms developed by themselves.
  • Google and the majority of search engines use machine learning. The expression proposals to be entered but also the results are based on a form of autonomous deduction.
  • The social networks Facebook and Twitter rely heavily on Machine Learning. The news feed adapts according to the subscriber’s preferences, but also taking into consideration their profile.
  • Voice assistants such as Siri and Alexa are examples of the usefulness of Artificial Intelligence in everyday life. These connected speakers learn and end up better understanding the user’s requests through interactions. They feature scalable natural language processing (NLP) technology.

Machine learning is mainly used for prediction

Platforms operating from the Internet are the pioneers of Machine Learning. These web giants explore and analyze large quantities of personal data to be able to establish logic. Each user thus has their own “patterns”. Their habits determine the kind of films or content that will be offered to them.

On the lookout for the slightest usable information, large companies pay a lot of attention to links clicked, publications made, or comments left. The Machine Learning algorithm feeds on data to be able to transform it into personalized deduction. All you need to do is watch a dance video to have hundreds of suggestions for content focused on choreography.

Regardless, digital entertainment providers aren’t the only ones leveraging machine learning. For good reason, robot vacuum cleaners are equipped with Machine Learning programs. They recognize autonomously to avoid areas where their wheels can get stuck. The same is true for driverless cars, GPS and voice input applications. All of these fields have in common that they use a Python programming language for their Machine Learning.

A scientific approach in 4 steps

Machine learning programs go through four main stages.

  • First, the data scientist defines the training data. This is the information that the algorithm will use to establish deductions. Tagging can help the software quickly find recurring characteristics to remember. In all cases, the manipulator should prepare, organize and clean the data to avoid having biased results.
  • The second step involves developing the algorithm itself. Sometimes there are catalogs where the data scientist can find the tool he needs. The type of program depends on the type and size of the training data to be worked on. The requests will also count in the decision.
  • With the third level, the human primes the algorithm for its task. It is a set of executions with a view to results comparable to the desired model. Technical parameters such as weight or bias provide high precision. Several types of variables are tried until the expected deductions are obtained.
  • In the fourth stage, the software enters a phase of perfecting what it already knows how to do. It is possible to add other problems to resolve. The new maps of a room allow the robot vacuum cleaner to quickly understand that furniture has been moved.
A lire également  Definition: Deep Learning

Several dedicated algorithms for machine learning

The algorithms that can dissect labeled data are the most numerous. That said, new programs are emerging regularly. Indeed, each Machine Learning software responds to a specific application.

  • So-called linear regression models are commonly used for performance predictions. For example, they make it possible to determine the turnover that a salesperson brings in based on his qualifications.
  • For a logistic regression algorithm, binary variables come into play. Several parameters will converge into a pair of results. A variant called support vector machine is used when the classification of the different criteria is simply too complex or impossible.
  • However, the logical path called a decision tree remains the most popular of all. This algorithm establishes various recommendations based on rules for classified data. Example: the application predicts the score of a match based on the age of the players or the statistics of victories between the two teams.

With unlabeled data, there are also some broad trends.

  • “Clustering” manages groups with similar characteristics. Registration is conditional on membership. Tools such as K-means, TwoStep or Kohonen are among the best known.
  • Association algorithms determine patterns and correlations based on the if/then condition. These programs are commonly used in Data Mining where a huge amount of data must be dissected.
  • More complex than all others, neural networks are multilayered. The data is peeled like an onion to understand its deeper logic. These are specific tools for Deep Learning.

Three distinct branches for this computer science discipline

With Machine Learning: learning can be supervised or not. In the first case, the data is labeled to allow the algorithm to more easily follow the path to follow. The software seeks to confirm a model entered in advance. Information can be classified. The program also just compares. On the other hand, it takes a lot of work to label the training data. These can sometimes be biased and lead to unreliable results.

For unsupervised learning, it is raw data that must be dissected. The program explores them in search of one or more logic to remember. The algorithms identify the relevant characteristics and then move on to labeling. They also take care of real-time triage and classification. This entire process is carried out without any human intervention. Which further reduces the risk of error. Its complexity makes it invulnerable to cyberattacks.

Semi-supervised learning is considered a compromise between the two methods. Partially labeled data serves as guides for classification and extraction of different features. This makes it possible to circumvent difficulties specific to Machine Learning which rely on mathematical rigor. Reinforcement learning does even better since the algorithm notes each error it has identified to know the exact result. It is the lethal weapon with which the computer ends up beating the human in any game.

neural network

Deep Learning and neural functions

Although it is still little known, Deep Learning is machine learning that emerged in the 1980s. Geoffrey Hinton laid its foundations in 1986. Since then, this independent branch has gained in power and performance. This is the approach that allows us to understand the most subtle logics. Experts also call it deep learning through neural networks. The data is analyzed in cascades like in the human brain.

Other forms of Machine Learning are commonly used in various applications. This ranges from children’s toys to industrial robotics. On the other hand, neural learning is still a privilege for data science. This is the panache of predictive software. Programs are capable of anticipating complex events. Thus, on-board computers in self-driving cars do not just avoid hitting other road users. They manage to bypass the traffic lanes with a possible traffic jam forming.

In Deep Learning, algorithms digest data to draw conclusions in the form of statistics. If the information changes, the machine is able to make a decision without any human intervention. Home automation, cooking, telecommunications and other applications are already benefiting from this. On the other hand, the system seems not to be completely ready since it still requires an operator behind it. Bus drivers will barely have less work with autonomous buses.