TECHNOLOGIA OCHRONY ŚRODOWISKA, GRUPA 1

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How exactly does it work imagine you have a huge recipe book. Each recipe has a list of ingredients and instructions on how to cook them. In the context of machine learning the ingredients are the input data and the instructions are the algorithm. The goal is to find the best “recipe” that produces the desired result. There are different types of learning in machine learning including supervised learning unsupervised learning and reinforcement learning. Supervised learning occurs when the algorithm is “trained” on a set of labeled data I.E. Data for which we already know the correct answer. Unsupervised learning on the other hand deals with unlabeled data and tries to identify hidden patterns or structures within it.

Reinforcement learning relies on a system of rewards and punishments to “teach” the algorithm how to behave in certain situations. Practical applications of machine learning the potential of machine learning is almost endless and is already transforming the way we live and work. For example the mobile app designs service recommendation systems used by platforms such as netflix or amazon are based on machine learning algorithms to suggest films tv series or products based on users' preferences and past behavior. Machine learning is also having a significant impact in the healthcare sector helping doctors diagnose diseases more accurately or predict the onset of medical conditions before they become serious.



Another sector that is benefiting enormously from machine learning is the financial sector. Banks and financial institutions use advanced algorithms to detect fraud optimize trading strategies or provide personalized financial advice to customers. But these are just a few of the countless applications of machine learning as technology advances and data availability increases we are likely to see even more innovations and transformations in almost every sector of the economy.
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