Machine Learning
1 — What is Machine Learning?
2 — How Does Machine Learning Work?
February 3, 2024
What is Machine Learning?
For a long time, computers have been solving complex tasks beyond human capabilities. However, the advantage of machines lies in their speed. The data a computer can process would take a human a significant amount of time to organize. Thus, specific algorithms emerged to 'teach' computers necessary commands to achieve automation.
Machine learning is a method that allows computers to 'learn' without programming or complex operations to perform required tasks. It is based solely on patterns and predefined logical chains. In other words, the machine is shown an example of a task, and instead of requiring identical performance, it is expected to find similar solutions.
AI leverages human experience in its learning process, mastering new technologies through examples of how humans tackle specific tasks. These examples are known as datasets. Using datasets, developers and programmers teach AI to format, classify, process, and organize any information. Datasets are a unified ecosystem, the lifeblood of AI, without which its existence would yield little impact.
Many of you use Yandex.Music. Every day, the app surprises you with new playlists tailored to your musical taste. Developers have taught AI to collect user data and offer alternative playlist options based on preferences.
How Does Machine Learning Work?
Machine learning is categorized into four main types: Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, and Reinforcement Learning. Each model uses several algorithmic integration methods simultaneously. The primary tasks of machine learning algorithms include classifying information components, finding patterns, and making informed decisions.
Supervised Learning
Algorithms in this model train AI based on specific examples. Supervised learning always involves 'input' and 'output' data. For instance, the AI must understand the difference between a town and a city. From the general data stream, the desired result in this pair is a city, which will be initially marked as the correct answer. To make the AI choose correctly, we need to show it differences using concrete examples.
Over time, the algorithm helps the system group training data (i.e., compile) and identify relationships between elements within these groups (i.e., detect correlation similarities).
Unsupervised Learning
In this learning model, there is no key to solving the task. The machine is introduced to data that lacks structure and labels. Therefore, AI independently identifies correlation patterns, selecting suitable and logical options. Unsupervised learning trains AI to recognize faces (biometrics) or conduct market analysis and create forecasts.
Semi-Supervised Learning
This model assumes a more realistic approach to working with data. In an ideal world, AI would instantly process data without errors detected later during validation. However, given the vast amount of unstructured and illogical data, it's impossible to organize everything perfectly on the first attempt. Therefore, semi-supervised learning involves introducing a small amount of labeled data. In this model, data is entered 'in portions.'
Reinforcement Learning
This model does not provide task solutions as in supervised learning but tests a set of permissible actions. A real-life example is playing chess (thousands of possible moves to achieve one goal—victory). A notable example of this learning model is stock trading or bidding for key phrases in Yandex.Direct auctions.
Where Is Machine Learning Currently Applied?
Now that we've discussed what machine learning is and how it works, let's talk about where it is used.
Healthcare. AI and various algorithms enable early-stage disease diagnosis.
Industry. This sector has vast amounts of data that must be processed within days or even hours. AI has learned to handle this task.
Self-driving cars. An innovation already legalized in Moscow, the Moscow region, and Tatarstan. Autonomous vehicles are driving around Moscow, but the algorithms are not yet perfect. Once developers refine the idea, the project will roll out in all cities, as the stakes are high—human lives.
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