The The use of machines has become an integral part of everyone’s daily life.and in humanity’s search for an easier and more productive life, machines have been created capable of learning on their own.
This is what it’s all about machine learning, an advanced branch of programming that gives computers the ability to learn without being programmed to do soin order to offer a more optimal service to people.
In the next few paragraphs, we will delve into the topic of machine learning and in all the learning systems applied by the machines you use in your daily life.
What is machine learning and what is this branch of artificial intelligence for?
It is a branch of artificial intelligence that allows machines to decipher patterns in order to make predictionsessentially learning from the user with little to no supervision.
In the middle of the year 2021, this technology was applied to a large number of everyday devicesas the recommendations in platforms like Netflix or Spotifyand the smart responses from either . In short, is a series of protocols that allow a machine to develop prediction algorithms that adapt to user behavior and the ability to predict it to provide faster and more personalized services.
How does machine learning work and how much does it influence our experience on the Internet?
Unlike classic computing procedures, where for a machine to perform an action it was necessary to program it with specific algorithms, the algorithms used in the machine learning they perform a large part of the actions on their own. These perform automatic calculations based on information collected from the user’s interaction with the system, and the more data they collect, the more accurate the calculations will be and the better the predictions will be.
Today, machine learning is so ingrained in most aspects of daily life, that it is practically impossible to use the internet without resorting to it. For example, machine learning is present in the smart recommendations from google searchthe detection of faces of your Smartphone, the one in your emailamong many other aspects.
History of machine learning When was it born and what were the first programs developed?
Although its use has expanded recently, the origins of machine learning date back to the 1950s, when the famous Alan Turing created the “Turing Test”, a test that, in order to pass it, forced a machine to fool a human into thinking they were in front of another person. Later, in 1952, Arthur Samuel wrote the first dynamic algorithm capable of learningby creating a program to play checkers that improved his strategies with each game played.
Later, at a conference held in 1956, the term “Artificial Intelligence” was first coined as the official name for this new field of computing that was being studied. The 70s were a difficult time for this branch of technology, due to the irrational expectations of investors, compared to the few discoveries made.
This until 1967 when the algorithm was created Nearest Neighbor”, a form of supervised learning that was used for prediction, or classification of new samples. The first rule-based dedicated systems were born in the 1980s, shortly before interest in the machine learning was drastically reduced for the second time, losing support until the early years of the 21st century.
in the early years 2000, specifically in the year 2003, Google creates a file system called “Google File System” and in 2004 it finished its file processing system, called “Map & reduce”. Later, in 2006, engineers from the Appalachian University completed Google’s data processing model. with the first Big Data platform from , called Hadoop.
Currently, the machine learning forms an integral part of the business worldas it is present in applications in all kinds of sectors, from small start-ups, to large companies that update their procedures.
Machine learning algorithms What are they and what types exist so far?
The machine learning is made up of a wide variety of algorithms that provide different rates and procedures for learning to the machines that use them.
Among the most used are:
supervised
The supervised machine learning is a modality of machine learning that allows researchers to understand how algorithms learn to interpret information entered by users.
Among the main characteristics of this modality of machine learning are:
- The introduction, labeling and classification of the data processed by the algorithm require human intervention.
- The output data of the algorithms are expected, since the input data has been classified by the person entering it.
- This algorithm supports the introduction of classification data, that allow to classify an object in different classes; and regression datawhich allows you to predict a numeric value.
East type of machine learning facilitates the prediction of point data and specific to obtain expected results.
Among the practical applications of this type of learning we can highlight:
- Insurance companies: they use it to determine the costs of a claim.
- Bank entities: allows them to perform fraud assessments and detection
- Companies in general: It allows to anticipate the breakdown of machinery.
not supervised
Unlike the supervised form, this type of algorithms do not require human intervention. The entered data contains unclassified elements, and the algorithm automatically looks for patterns among them.
Among its most outstanding features are:
- The data entered they are not classified or labeled.
- Does not require human intervention to work.
- It includes algorithms clustering, which classifies the output data into groups, and association that deciphers rules and patterns within the entered data.
Practical applications for unsupervised Machine Learning respond to other types of demands:
- entities banking: It is used to classify bank customers by type.
- Health centers: classifies patients according to different criteria.
- Streaming platforms: forms the recommendation system for a series or music transmission platform.
Reinforced
Is a behavioral psychology methodology transferred to machine learning, in which machines learn on their own to follow a set of rules and behaviors based on rewards and penalties.
The main characteristics of the reinforced larynx machine are:
- It is based on reinforcement learning behavioral psychology.
- Gives the machine the ability to make decisions based on experience.
- It focuses on the development of a system, called “Agent”which seeks to improve their efficiency when interacting with their environment and the use of rewards to modify their behavior.
Some of the practical applications for reinforcement learning algorithms are:
- Navigation systems: it is used in the navigation systems of autonomous vehicles, drones and even robots.
- Refinement of designs: It is used in the manufacture of parts and the selection of materials to reduce costs or increase quality.
- Resource management: allows you to manage stock records or staff rotation shifts.
Semi-supervised learning
It is a machine learning methodology that employs both categorized and uncategorized data entry.
Typically, a small portion of the former along with a larger portion of the latter can greatly improve the accuracy of learning:
- only require partial human supervision.
- The labeled data corresponds to a small part of all training data entered.
- their algorithms they meet the same objectives as supervised and unsupervised learning.
The practical applications of this type of machine learning are similar to those of supervised and unsupervised learningwith the difference that it can be more accurate and cheaper than the other two modalities.
transduction
It is a learning system with similarities to supervised learning, but with the difference that its learning criteria, that is, the input data, they are used for the prediction of future data based on their categories and comparing them with the new input data. Its most viewed practical application is in the predictive texts of some smart keyboardas well as predictions from search engines like Google.
Multitask
It is a learning method focuses on the use of previously acquired data to solve problems of a similar nature to those already seen. In short, you solve multiple learning tasks simultaneously while looking for similarities between them to improve your efficiency when solving future tasks. This method is one of the fastest for machine learning and provides an improvement in learning efficiency and prediction accuracy.
Machine Learning Techniques What are the main strategies used to teach machines?
Inside of the machine learning a large number of techniques are used to enter the information to algorithmsin order to allow them to perform their own calculations, regardless of the classification of the information entered.
Let’s see next:
Regression
East method corresponds to supervised machine learningand consists of the prediction explanation of a numerical value using previously entered data or sets as a base. For example, an algorithm using regression learning can calculate the value of real estate based on input data that represent previous prices for properties with similar characteristics.
Decisions Tree
Use a decision tree as a predictive model for mapping observations about the data entered with the aim of reaching a final conclusion about said data. It is one of the most effective methods to keep track of machine learning progress, since it allows to visualize previous decisions and compare them with future problems.
Group
It falls under the category of unsupervised machine learning, its objective is to make the machine group or classify data sets with similar characteristics, allowing the algorithm to perform this classification automatically. Because it is an automatic process performed by the algorithm, the only way the quality of solutions to problems can be assessed is…
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