Over the past few years, machine learning (ML) has quietly become an integral part of our daily lives. It impacts everything from personalized recommendations on shopping and streaming sites to protecting our inboxes from the onslaught of spam we get every day. But it’s not purely a tool for our convenience. Machine learning has become crucial in the current tech landscape, and that doesn’t look like it’ll change any time soon. It’s used to unlock hidden insights in data, automate tasks and processes, enhance decision-making, and push the boundaries of innovation.
At the core of this technology are machine learning algorithms. These are essentially the computer programs that are designed to learn from data without being explicitly programmed for the individual tasks. They are used to continuously analyze information, adapt their structure, and improve over time.
In this article, we’ll run through 11 popular machine learning algorithms and explain what they do and what you might use them for. To make this easier, the list is broken down into four categories:
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Supervised learning
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Unsupervised learning
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Ensemble
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Reinforcement learning
By the end of this article, you’ll have a better understanding of what machine learning algorithms can do and the different strengths and weaknesses of each one.
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