In recent decades, the technology industry has seen the most growth in areas of Artificial Intelligence and, more specifically, Machine Learning. In a world where data has become a highly prized commodity, Machine Learning has acquired enormous relevance in the current tech ecosystem.
The main purpose of machine learning is to provide machines with the ability to learn entirely by themselves, without the need for anyone to perfect their algorithms. The goal is that, like the human mind, they can improve their own processes so that they can perform the tasks that have been entrusted to them with an ever greater degree of precision. In order for machine learning to reach its ideal state, it is necessary to provide the machine with certain information either by supplying the necessary information through files loaded with a multitude of data or by allowing the machine to gather data through its own observations and even interact with the real world.
This type of learning through data collection and interpretation has allowed them to advance from relatively simple tasks to more complex ones. Initially, they were prepared to filter emails or perform other daily actions, but over time they have been given greater computing power and today they are capable of analyzing much more complex tasks. Now they are capable of analyzing millions of medical diagnostic results which can, among other benefits, allow us to detect cancers more reliably; the can predict traffic patterns allowing us to plan routes with sufficient notice; they can even carry out architectural projects in real-time, just by recognizing a certain area.
In artificial intelligence or AI, there are three primary types of machine learning algorithms employed depending on how the machine will be trained and instructed to improve how its task performance. The end-goal is for the machine to execute actions in an increasingly optimized manner by refining patterns and behavior through continuous learning. The three main types of learning algorithms are Supervised, Unsupervised, and Reinforcement. In order to decide which type of machine learning is needed, it is fundamental to know precisely what the purpose is that we want to achieve by programming that artificial intellect.
This type of machine learning is about providing machines with prior information so that they have initial examples and can expand their knowledge over time. It is usually done by means of labels, meaning that when we program the machines we pass them properly labeled elements so that later they can continue labeling new elements without the need for human intervention. For example, we can pass the machine pictures of cars, buildings, traffic signs, or anything relevant to our task, then we tell it what each item is and how we want it to be interpreted. With these initial examples, the machine generates its own supply of knowledge so that it can continue to assign labels when it recognizes a car, a building, or a traffic sign.
In this type of machine learning, the machines are not limited to being trained from images but can use various data types. If they are supplied with sounds or calligraphy data sets, they can learn to recognize voices or detect written patterns and associate them with a particular person. The possibilities stem entirely from the initial data that is supplied to the machine.
In this case, the machine is not provided with any kind of previously labeled information about what it should recognize, meaning that it will not have an existing knowledge base. Instead, it is provided with data regarding the characteristics of the thing it is meant to identify and it would then have to learn to recognize those characteristics on its own. Essentially, this type of learning algorithm requires the machine to develop its own knowledge base from a limited data set.
Believe it or not, this is closest to the way the human mind learns and develops. The machine learns to analyze groups using a method known as clustering. This is nothing more than grouping the elements according to a series of characteristics they have in common.
This type of automatic learning is used when the aim is for systems to learn from acquired experiences. In these cases, when humans program the algorithm, they define what the final result should be without indicating the best way to achieve it. Thus, the machine is responsible for discovering how to achieve its goal. The machine is in charge of carrying out a series of tests in which it obtains errors and successes, learning from the latter and discarding those other actions which led to failure. In short, it detects patterns of success that it repeats over and over again to become increasingly efficient.
Autonomous cars are a good example of this type of learning algorithm. Their task is very clear: take passengers to their intended destination. As the cars make more and more journeys, they discover better routes by identifying shortcuts, roads with fewer traffic lights, and more. This allows them to optimize their journeys and, therefore, do their work more efficiently.
Although there are many who consider the two areas to be interchangeable, the truth is that Machine Learning and Deep Learning are very different disciplines. In broad terms, the latter is a kind of subset of machine learning, since its technical conception and its practical application are nothing more than a form of Machine Learning. This is why some people often confuse the two terms or use them interchangeably.
Essentially, what Deep Learning does is to structure algorithms so that they form an artificial neural network, which allows it to both learn and make decisions autonomously. This is precisely where the main difference between the two technologies lies. For a machine programmed with some kind of Machine Learning, a human must amend errors made by the machine by adjusting the configuration to prevent it from failing again. However, a Deep Learning model can determine by itself if its task has been completed successfully or not, by utilizing its own neural network.
In the end, as you can see, there are nearly endless possibilities that arise from the exponential growth of Machine Learning. This is why technology companies are increasingly seeking out candidates with extensive knowledge in Data Analytics. For those that are passionate about AI and Machine learning, the Ironhack Data Analytics Bootcamp serves as a gateway to advancement in this fascinating technological world.
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