Machine learning types represent yet another fantastic innovation that digital transformation brings to companies through artificial intelligence.
There are four main types of this technology critical to broadening insight into customer behavior, assisting in new product development, and improving business operation standards.
There is no denying that this technology is crucial for a company, do you agree? So, get ready to learn more about the concept of machine learning and its main types!
What Is Machine Learning?
In Portuguese, machine learning (or machine learning) is a type of artificial intelligence (AI) that generates more accurate applications in predicting results without being explicitly programmed for it.
Machine learning algorithms use historical data as input to predict new output values. Recommendation engines are an everyday use case for machine learning. Other popular uses include:
- fraud detection;
- spam filtering;
- malware threat detection;
- business process automation (BPA);
- predictive maintenance.
What Is The Reality Of This Technology In Companies Today?
Machine learning is still a complicated innovation for Brazil. The tool has great potential for development, but the investment value is considered high for what it can deliver to the business.
Despite this, the technology will likely gain strength and gradually improve with more realistic values and response precision. Unsupervised learning, for example, is considered risky, as the machine is completely free to perform the analysis, which decreases reliability.
However, market giants such as Google, Amazon and Apple have been adopting technologies on top of:
- internet of things (IoT), or internet of things ;
- cybersecurity enhancement ;
- automation of natural speech understanding ;
- ethics in artificial intelligence.
All this has been collaborating to simplify and democratize access to artificial intelligence and make it able to improve business goals and improve the development of products and services.
What Are The Main Types Of Machine Learning?
When a data scientist evaluates which types of machine learning to use, he ponders the kind of data he wants to predict. From this, he defines the type of algorithm to be used.
See in the following topics what these types of machine learning are, how they apply, and what their advantages are in a business project.
Supervised
In this type of learning, data scientists provide algorithms with labeled training data and define the variables they want the algorithm to evaluate for correlations. Both the input and output of the algorithm are specified. Some of the most common are:
- linear regression;
- logistic regression;
- decision tree.
There are several advantages to applying supervised learning, such as leveraging experience to collect data or produce output, optimizing criteria through experience, and solving various real-world problems.
Unsupervised
The unsupervised type of machine learning involves algorithms that train on unlabeled data. The algorithm scans the datasets looking for any significant connections. The data that the algorithms train and the predictions or recommendations they produce are predetermined. Here are some of the main benefits of this model:
- very useful for finding patterns in data that are not possible to find using standard methods;
- classification and learning of unlabeled data;
- aid in understanding raw data.
Semi-Supervised
Regarding the semi-supervised type, the main objective is to merge the two previous possibilities. Data scientists can feed an algorithm mostly labeled training data, but the model is free to explore the data and develop its understanding of the dataset.
The main advantage of semi-supervised learning is expanding the potential of the supervised model. Data labeling does not always cover all probabilities as it is a more expensive and complex process.
So, semi-supervised learning allows the exploitation of unlabeled data for it. In this way, you can expect a better and more complete response scenario than using only a supervised or an unsupervised model.
By Reinforcement
In this model, data scientists often use reinforcement learning to teach a machine to complete a multi-step process with clearly defined rules. Among the main advantages, we highlight:
- performance optimization;
- sustaining change over a long period;
- overload decreases.
Data scientists program an algorithm to complete a task and provide positive or negative clues as they figure out how to complete a task. But most of the time, the algorithm decides on its own which steps to take along the way.
What Is The Relationship Between Machine Learning And Cybersecurity?
Imagine how challenging it is for large businesses to regularly live with millions of malware attacks. How would one or several human programmers manage to combat this every day?
In this scenario, machine learning is critical, as it uses algorithms from previous datasets and statistics to generate assumptions about a machine’s behavior. The equipment can adjust its actions and even execute them without being programmed for it.
It has the potential to classify millions of files and track down potentially dangerous ones. At this point, it becomes an essential tool for discovering threats and eliminating them before their action.
Also Read: Understand The Benefits Of Machine Learning!