Machine learning algorithms

Machine learning is an integral component of artificial intelligence (AI), it uses statistical methodologies to empower machines to learn and perform tasks on their own, that is, without the need for direct instructions and explicit programming.

The basis of machine learning is its algorithms. They are complex sets of rules and mathematical models that train machines so that they can analyze information, draw conclusions and make informed decisions.

Machine learning algorithms are actively used for predictive modeling, which plays an important role in a variety of applications, ranging from personalization of recommendations on streaming platforms to forecasting trends in the stock market.

Machine learning algorithms can be divided into the following categories

Supervised learning

Algorithms are trained on pre-labeled training data and extrapolate this training to predict results for unseen data. The mechanism is similar to the teacher-student relationship, when the algorithm (student) receives knowledge from the teacher (data) and applies it to new scenarios.

Unsupervised learning

Algorithms work with unlabeled data, independently identifying patterns and structures. This can be compared to a researcher who travels through an unknown territory without any prior knowledge in order to get to know the area and make discoveries in the process.

Reinforcement learning

Algorithms learn based on the results of their actions, in fact, in the process of their trial and error. They perform actions in a specific environment in order to maximize the reward signal. If you transfer this to everyday life, then “reinforcement learning” can be compared to how a dog is taught to give a paw.

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