Machine learning methods and techniques

 That is, Machine learning is with us for a long time. The technology will continue to be implemented in areas of human life: predicting, classifying, generating objects. Therefore, it is important to keep up and understand what machine learning is, how ML appeared, what methods and techniques it uses. We will talk about this in our article.

The essence of machine learning

Machine learning is a direction of development of  artificial intelligence that imitates human thinking processes. Here, a clear sequence of actions is not set, which, for example, is performed by software, but constant “thinking” occurs, as the brain would do.

Machine learning is forecasting based on huge amounts of data, in which algorithms find patterns. The concept is associated with neural networks, which are one of the types of ML and work through deep learning.

Machine learning algorithms are used to create services that:

• Recommend products, services and content , including based on user actions. For example, this is how online cinemas work, offering films and TV series based on what you have watched.
• They predict the future: possible trends, sales volumes, illnesses of medical clinic clients based on their medical history, events, etc. Machine learning models determine credit ratings in banks and predict the behavior 
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• of clients who may not be able to repay a loan.
• Recognize objects in images and videos, understand speech and analyze text . This speeds up routine processes, simplifies human work and even ensures safety. For example, identifying car numbers from surveillance cameras is additional control.

How Machine Learning Works

1. Defining criteria for selection and collecting data . This is a huge amount of information.
2. Preparation of information — delimitation of it with labels, which are important for recognition of the necessary elements by ML algorithms. Currently, labeling is carried out by specialists and is less often automated, so this process is long.
3. Checking data and finding patterns. This is where errors are found to correct them and make the next step more accurate.
4. Selecting a model and starting training. The algorithm processes the data and produces results.
5. Receiving and evaluating the work of algorithms. At this stage, errors are corrected, algorithms are changed for further work.