Machine Learning: Discover How To Apply It To Your Business
Machine Learning: The digitization of processes has boosted the emergence of various technologies. They facilitate our routine and bring a competitive advantage to companies in the corporate environment. In this context, it is vital to use machine learning.
This is one of the technological trends of the globalized world and was created within the scenario of digital transformation. Machine learning allows you to automate actions to provide a better user experience.
What Technologies Relate To Machine Learning?
To understand machine learning, you need to know which technologies are related.
AI is directly related. That’s because machine learning is a subset of artificial intelligence. All machine learning systems are part of AI, but the reverse is not valid.
The difference between these terms is that machine learning is one of the pillars of AI. In turn, the latter is a vast technological field defined by studying ways to simulate human intelligence.
Therefore, AI has several subfields. The three main ones are:
- Computer Vision ( Computer Vision );
- Natural Language Processing (NLP);
- machine learning.
It is a subfield of machine learning. Therefore, machine learning is within AI, and deep understanding is embedded in this first concept. But what does it mean in practice? This term refers to a neural network formed by different layers that help to achieve better results.
With this, it is possible to adopt a predictive learning model. That is, inserting data by a programmer or even the passive collection of information is no longer necessary. The machine itself understands the context and finds solutions early in deep learning.
Internet Of Things (IoT)
The Internet of Things is a technology that allows objects to connect to the internet. From there, they collect and transmit data to the cloud. In this scenario, machine learning ensures you get data to anticipate situations.
This is the case of predictive maintenance. From the historical data generated by the equipment, it is possible to predict when the machines of a factory will stop and change parts. Or also the necessary repairs before operations are halted.
It is a concept related to an extensive online data repository generated daily by internet users. Big data also ensures the collection, organization, analysis, and accessibility of these items to be transformed into information and insights.
Therefore, big data is related to machine learning by providing the necessary data. In this way, intelligent machines learn the functions and gather information.
What Are The Main Machine Learning Methods?
As we have already explained, each algorithm has a specific learning focus. Therefore, different types of methods can be applied in machine learning. In some places, you will find varieties of this technology. Anyway, they are synonyms. See what they are!
It is the model in which a predetermined data set is entered into the system. It already contains the correct answer. Therefore, learning is supervised, as problems and solutions are defined and associated.
In this scenario, the machine presents the correct result from the variables. This is the case with Google image search: the algorithm finds the origin of the file and searches for similar ones.
Here, it is the opposite of the previous method. That is, there is neither a specific expected result nor a correct answer. This is because the data crossing is unpredictable and changes according to the variables placed in the system.
For this reason, it is a more complex process. This is the case of a consumer habits survey, which gathers much information—for example, purchase frequency, records, customer profile, etc. Thus, patterns are found in these data, and the result is obtained, which is impossible to predict.
It is a method that combines the two previous types. So there is a certain amount of definite answers, but there are also a lot of uncertainties. Thus, these unsupervised variables drive the discoveries that the machine will make.
It is the machine learning method where algorithms teach a model. Thus, it is possible to give positive reinforcements for the expected results and negative support for the unwanted ones. Therefore, the system receives feedback similar to rewards and punishments.
This is the method applied to a game. Whoever is learning is rewarded or punished. It all depends on your successes or mistakes.
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