Machine learning vs traditional programming

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People often ask: How does machine learning differ from traditional programming?

Today, machine learning has emerged as a powerful tool that has revolutionized various industries. In a way, traditional programming has done the same.

But how do the two differ from each other?

Traditional programming:
This involves explicitly instructing a computer to perform a specific task. Developers write lines of code that outline a series of steps to be executed. This method is deterministic, meaning the output is solely based on the input and the predefined set of rules. It is like following a recipe, where the outcome is predictable and consistent.

Machine learning: This takes a different approach. It is an application of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. Instead of relying on predefined rules, machine learning algorithms analyze vast amounts of data to identify patterns and make predictions or decisions. It is a probabilistic approach, where the output is based on statistical inference and the algorithm’s ability to generalize from the data it has been trained on.

Lesson: Traditional programs are static and require manual updates to accommodate changes or new scenarios.

Lesson: Machine learning models can adapt and improve over time as they encounter new data. This flexibility allows machine learning systems to handle complex and dynamic tasks more effectively.

We don’t know much yet, but the world could gravitate more and more into machine learning in the future. Mobile phones, video games, and other technology fields may rely heavily on machine learning in the future.

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